pubs2024

@inproceedings{Miranda2023826,

title = {Data Platforms for Real-time Insights in Healthcare: Systematic Review},

author = {R. Miranda and C. Alves and A. Abelha and J. Machado},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164491019&doi=10.1016%2fj.procs.2023.03.110&partnerID=40&md5=a066521d09ece0ce9d718e34c451fe31},

doi = {10.1016/j.procs.2023.03.110},

issn = {18770509},

year  = {2023},

date = {2023-01-01},

journal = {Procedia Computer Science},

volume = {220},

pages = {826-831},

publisher = {Elsevier B.V.},

abstract = {The ever-growing usage and popularity of Internet of Things devices, coupled with Big Data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, there is a need to reduce the gap on time from when information is created to when actions and insights can be offered. However, a challenge in implementing a large-scale data processing architecture is deciding which tools are appropriate, and how to apply them in the best way possible. For example, streaming systems are now mature enough that hospitals worldwide can use their extremely large datasets, along with data producers, to predict and influence future events. Thus, the main objective of this systematic review is to identify the state-of-the-art in data platforms on healthcare that allow the creation of metrics and actions in real-time. The PRISMA guideline for reporting systematic reviews was implemented to deliver a transparent and consistent report, validating the technological advances in a critical sector. Multiple pertinent articles and papers were retrieved from the SCOPUS abstract and citation database on May 13, 2022, using several relevant keywords to identify potentially relevant documents published from January 2020 onward. These documents must have already been published in English and been already published, and accessible through the B-ON consortium that allows Portuguese students to legally download from most publishers. Over seven studies have been selected for deeper discussion based on their relevance and impact for this review, showcasing their main objectives, data sources, and tools used, as well as their approaches for interoperability and support of machine learning algorithms for decision support. In closing, the collected articles have shown that while Big Data is currently in use at health institutions of all sizes, the ability of processing large amounts of data from sensors and events, and notifying stakeholders as quickly as possible is still in its infancy. © 2023 Elsevier B.V.. All rights reserved.},

note = {cited By 0; Conference of 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 ; Conference Date: 15 March 2023 Through 17 March 2023; Conference Code:189712},

keywords = {Business-intelligence; Data engineering; Data lake; Data mining event-driven microservice; Data platform; Event-driven; Machine-learning; Real- time; Streaming systems; Systematic Review, Computer architecture; Data Analytics; Data handling; Decision support systems; Health care; Large dataset; Learning algorithms; Learning systems; Machine learning; Real time systems, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Marques202263,

title = {Predicting Diabetes Disease in the Female Adult Population, Using Data Mining},

author = {C. Marques and V. Ramos and H. Peixoto and J. Machado},

editor = {Goleva R. Silva B. Spinsante S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127917985&doi=10.1007%2f978-3-030-99197-5_6&partnerID=40&md5=d0b1061ffb8b1c4ae3d164d82c9a4a30},

doi = {10.1007/978-3-030-99197-5_6},

issn = {18678211},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {432 LNICST},

pages = {63-73},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The aim of this study is to predict, through data mining, the incidence of diabetes disease in the Pima Female Adult Population. Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces and is a major cause of blindness, kidney failure, heart attacks, stroke and lower limb amputation. The information collected from this population combined with the data mining techniques, may help to detect earlier the presence of this decease. To achieve the best possible ML model, this work uses the CRISP-DM methodology and compares the results of five ML models (Logistic Regression, Naive Bayes, Random Forest, Gradient Boosted Trees and k-NN) obtained from two different datasets (originated from two different data preparation strategies). The study shows that the most promising model as k-NN, which produced results of 90% of accuracy and also 90% of F1 Score, in the most realistic evaluation scenario. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 0; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359},

keywords = {Adult populations; Chronic disease; CRISP-DM; Data-mining techniques; Female adults; Heart attack; Logistics regressions; Lower-limb amputations; ML model; Naive bayes, Barium compounds; Decision trees; Insulin; Logistic regression; Nearest neighbor search; Population statistics, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Duarte202253,

title = {A Comparative Study of Data Mining Techniques Applied to Renal-Cell Carcinomas},

author = {A. Duarte and H. Peixoto and J. Machado},

editor = {Goleva R. Silva B. Spinsante S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127847164&doi=10.1007%2f978-3-030-99197-5_5&partnerID=40&md5=df7b9e6c1d403d0e3049ede31cd77307},

doi = {10.1007/978-3-030-99197-5_5},

issn = {18678211},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {432 LNICST},

pages = {53-62},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Despite being one of the deadliest diseases and the enormous evolution in fighting it, the best methods to predict kidney cancer, namely Renal-Cell Carcinomas (RCC), are not well-known. One of the solutions to accelerate the current knowledge about RCC is through the use of Data Mining techniques based on patients' personal and clinical data. Therefore, it is crucial to understand which techniques are the most suitable to extract knowledge about this disease. In this paper, we followed the CRISP-DM methodology to simulate different techniques to determine the ones with the best predictive performance. For this purpose, we used a dataset of 821 records of RCC patients, obtained from The Cancer Genome Atlas. The present work tests different Data Mining techniques, that can be used to predict the 5-year life expectancy of patients with renal cancer and to predict the number of days to death for patients who have a life expectancy of less than 5 years. The results obtained demonstrated that the best algorithm for estimating the vital status at 5 years was Random Forest. This algorithm presented an accuracy of 87.65% and an AUROC of 0.931. For the prediction of days to death, the best performance was obtained with the k-Nearest Neighbors algorithm with a root mean square error of 354.6 days. The work suggested that Data Mining techniques can help to understand the influence of various risk factors on the life expectancy of patients with RCC. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},

note = {cited By 1; Conference of 8th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2021 ; Conference Date: 24 November 2021 Through 26 November 2021; Conference Code:275359},

keywords = {'current; Clinical data; Comparatives studies; CRISP-DM; Data-mining techniques; Kidney cancer; Life expectancies; Rapidminer; Renal cell carcinoma; Survival, Data mining, Decision trees; Diseases; Forecasting; Mean square error; Nearest neighbor search},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Guisasola2022271,

title = {Detecting Autism Spectrum Disorder Using Data Mining},

author = {A. C. Guisasola and D. Ferreira and C. Neto and A. Abelha and J. Machado},

editor = {Riola Rodriguez J. M. Fajardo-Toro C.H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119376845&doi=10.1007%2f978-981-16-4884-7_22&partnerID=40&md5=0b7fd71f51e6e6841168346ba356b94e},

doi = {10.1007/978-981-16-4884-7_22},

issn = {21903018},

year  = {2022},

date = {2022-01-01},

journal = {Smart Innovation, Systems and Technologies},

volume = {255},

pages = {271-281},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Autism spectrum disorder (ASD) is a set of neurodevelopmental disorders that affect cognitive development, social and communication skills, and behavior of affected individuals. The faster traces of ASD are identified, the faster the stimulation will begin and the more effective the gains in neuropsychomotor development will be. That being said, the earlier the diagnosis of ASD, the easier it is to control the disorder. Therefore, this study aims to classify the cases of ASD as “yes” if a patient has been diagnosed with ASD or “no” if a patient has not, using data mining (DM) models with classification techniques. The methodology of cross-industry standard process for data mining (CRISP-DM) was followed, and to induce the data mining models, the Rapidminer software was used. The results were quite promising reaching a level of accuracy of 97%, specificity of 95.45%, sensitivity of 100%, and precision of 95.65%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},

note = {cited By 0; Conference of Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference Date: 18 August 2021 Through 20 August 2021; Conference Code:267889},

keywords = {Autism spectrum disorders; Cognitive communications; Cognitive development; Communication skills; Cross industry; Cross-industry standard process for data mining; Data mining models; Industry standards; Social skills; Standards process, Classification (of information); Diseases, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Vieira2021511,

title = {Data Mining Approach to Classify Cases of Lung Cancer},

author = {E. Vieira and D. Ferreira and C. Neto and A. Abelha and J. Machado},

editor = {Dzemyda G. Adeli H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105949008&doi=10.1007%2f978-3-030-72657-7_49&partnerID=40&md5=071cf3523dd9f477acf20273d8405f0e},

doi = {10.1007/978-3-030-72657-7_49},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1365 AIST},

pages = {511-521},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {According to the World Cancer Research Fund, a leading authority on cancer prevention research, lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women, with the 5-year relative survival percentage being significantly low. Smoking is the major risk factor for lung cancer and the symptoms associated with it include cough, fatigue, shortness of breath, chest pain, weight loss, and loss of appetite. In an attempt to build a model capable of identifying individuals with lung cancer, this study aims to build a data mining classification model to predict whether or not a patient has lung cancer based on crucial features such as the above mentioned symptoms. Through the CRISP-DM methodology and the RapidMiner software, different models were built, using different scenarios, algorithms, sampling methods, and data approaches. The best data mining model achieved an accuracy of 93%, a sensitivity of 96%, a specificity of 90% and a precision of 91%, using the Artificial Neural Network algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 3; Conference of World Conference on Information Systems and Technologies, WorldCIST 2021 ; Conference Date: 1 April 2021 Through 2 April 2021; Conference Code:256979},

keywords = {Artificial neural network algorithm; Cancer prevention; Cancer research; Data mining models; Mining classification; Risk factors; Sampling method; Weight loss, Biological organs; Classification (of information); Diseases; Information systems; Information use; Neural networks, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neto2021210,

title = {Prediction Models for Polycystic Ovary Syndrome Using Data Mining},

author = {C. Neto and M. Silva and M. Fernandes and D. Ferreira and J. Machado},

editor = {Antipova T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103521052&doi=10.1007%2f978-3-030-71782-7_19&partnerID=40&md5=cd8917667f9e56f1982c1aba9ff64f02},

doi = {10.1007/978-3-030-71782-7_19},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1352},

pages = {210-221},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Polycystic Ovary Syndrome is an endocrine abnormality that occurs in the female reproductive system and is considered a heterogeneous disorder because of the different criteria used for its diagnosis. Early detection and treatment are critical factors to reduce the risk of long-term complications, such as type 2 diabetes and heart disease. With the vast amount of data being collected daily in healthcare environments, it is possible to build Decision Support Systems using Data Mining and Machine Learning. Currently, healthcare systems have advanced skills like Artificial Intelligence, Machine Learning and Data Mining to offer intelligent and expert healthcare services. The use of efficient Data Mining techniques is able to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to assist doctors in maximizing the accuracy of the diagnosis. In this sense, this paper aims to predict, using the classification techniques and the CRISP-DM methodology, the presence of Polycystic Ovary Syndrome. This paper compares the performance of multiple algorithms, namely, Support Vector Machines, Multilayer Perceptron Neural Network, Random Forest, Logistic Regression and Gaussian Naïve Bayes. In the end, it was found that Random Forest provides the best classification, and the use of data sampling techniques also improves the results, allowing to achieve a sensitivity of 0.94, an accuracy of 0.95, a precision of 0.96 and a specificity of 0.96. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 8; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499},

keywords = {Classification technique; Healthcare environments; Healthcare services; Hidden information; Multi-layer perceptron neural networks; Multiple algorithms; Polycystic ovary syndromes; Reproductive systems, Data mining, Decision support systems; Decision trees; Diagnosis; Diseases; Health care; Hospital data processing; Learning systems; Logistic regression; Multilayer neural networks; Predictive analytics; Random forests; Support vector machines; Support vector regression},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Abreu2021198,

title = {Diagnosis of Diabetic Retinopathy Using Data Mining Classification Techniques},

author = {A. Abreu and D. Ferreira and C. Neto and A. Abelha and J. Machado},

editor = {Antipova T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103501921&doi=10.1007%2f978-3-030-71782-7_18&partnerID=40&md5=00cbc654502f69e638aa251a4aad2b0f},

doi = {10.1007/978-3-030-71782-7_18},

issn = {21945357},

year  = {2021},

date = {2021-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {1352},

pages = {198-209},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Diabetic retinopathy is one of the complications of diabetes that affects the small vessels of the retina, being the main cause of blindness in adults. An early detection of this disease is essential, as it can prevent blindness as well as other irreversible harmful outcomes. This article attempts to develop a data mining model capable of identifying diabetic retinopathy in patients based on features extracted from eye fundus images. The data mining process was carried out in the RapidMiner software and followed the CRISP-DM methodology. In particular, classification models were built by combining different scenarios, algorithms, and sampling methods. The data mining model which performed best achieved an accuracy of 76.90%, a precision of 85.92%, and a sensitivity of 67.40%, using the Logistic Regression algorithm and Split Validation as the sampling method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 2; Conference of International Conference on Advances in Digital Science, ICADS 2021 ; Conference Date: 19 February 2021 Through 21 February 2021; Conference Code:256499},

keywords = {Classification models; Data mining models; Data mining process; Diabetic retinopathy; Eye fundus; Logistic regression algorithms; Mining classification; Sampling method, Computer aided diagnosis; Eye protection; Logistic regression; Medical imaging, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pinto2020562,

title = {Data mining to predict early stage chronic kidney disease},

author = {A. Pinto and D. Ferreira and C. Neto and A. Abelha and J. Machado},

editor = {Yasar A. Shakshuki E.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099880679&doi=10.1016%2fj.procs.2020.10.079&partnerID=40&md5=3e496eba589d1580b145c7043e27343e},

doi = {10.1016/j.procs.2020.10.079},

issn = {18770509},

year  = {2020},

date = {2020-01-01},

journal = {Procedia Computer Science},

volume = {177},

pages = {562-567},

publisher = {Elsevier B.V.},

abstract = {Chronic Kidney Disease (CKD) is a condition characterized by a gradual loss of kidney function over time. In national and international guidelines, CKD is organized into different degrees of risk stratification using commonly available markers. It is usually asymptomatic in its early stages, and early detection is important to reduce future risks. This study used the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and the WEKA software to build a system that can classify the chronic condition of kidney disease based on accuracy, sensitivity, specificity and precision. The results obtained were considered satisfactory, achieving the most suitable result of 97.66% of accuracy, 96.13% of sensitivity, 98.78% of specificity and 98.31% of precision with the J48 algorithm. © 2020 The Authors. Published by Elsevier B.V.},

note = {cited By 5; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555},

keywords = {Chronic conditions; Chronic kidney disease; CRISP-DM; Cross industry; Kidney disease; Kidney function; Risk stratification, Computer science; Computers, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gonçalves2020556,

title = {Prediction of mental illness associated with unemployment using data mining},

author = {C. Gonçalves and D. Ferreira and C. Neto and A. Abelha and J. Machado},

editor = {Yasar A. Shakshuki E.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099879347&doi=10.1016%2fj.procs.2020.10.078&partnerID=40&md5=077569e0b99ba95909110a8699069c1e},

doi = {10.1016/j.procs.2020.10.078},

issn = {18770509},

year  = {2020},

date = {2020-01-01},

journal = {Procedia Computer Science},

volume = {177},

pages = {556-561},

publisher = {Elsevier B.V.},

abstract = {Mental illness is a concern these days, affecting people worldwide and across all kinds of ages. This article aims to predict mental illness and discover its association with unemployment as well as other possible causes behind the illness. In order to accomplish this goal, a Data Mining (DM) process was performed using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the RapidMiner Studio software. In the end, the results obtained were considered promising since all the evaluation metrics, namely accuracy, sensitivity, and specificity, obtained values above 90%. The study also allowed, in the end, to identify the factors associated with the prediction of mental illness. © 2020 The Authors. Published by Elsevier B.V.},

note = {cited By 7; Conference of 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020 ; Conference Date: 2 November 2020 Through 5 November 2020; Conference Code:166555},

keywords = {CRISP-DM; Cross industry; Evaluation metrics; Mental illness, Data mining, Diseases; Employment; Forecasting},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peixoto2020209,

title = {Integrating a Data Mining Engine into Recommender Systems},

author = {V. Peixoto and H. Peixoto and J. Machado},

editor = {Camacho D. Novais P. Analide C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097363571&doi=10.1007%2f978-3-030-62362-3_19&partnerID=40&md5=742581dbe55b54d9a6d34bd6fbc8e28a},

doi = {10.1007/978-3-030-62362-3_19},

issn = {03029743},

year  = {2020},

date = {2020-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {12489 LNCS},

pages = {209-220},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {History could be epitomised to a handful of events that changed the course of human evolution. Now, we found ourselves amid another revolution: the data revolution. Easily unnoticeable, this new outlook is shifting in every possible way how we interact with the internet and, for the first time in history, how the internet interacts with us. This new kind of interactions is defined by connections between users and consumable goods (products, articles, movies, etc.). And through these connections, knowledge can be found. This is the definition of data mining. Buying online has become mainstream due to its convenience and variety, but the enormous offering options affect negatively the user experience. Millions of products are displayed online, and frequently the search for the craved product is long and tiring. This process can lead to a loss of interest from the customers and, consequentially, losing profits. The competition is increasing, and personalisation is considered the game-changer for platforms. This article follows the research and implementation of a recommender engine in a well-known Portuguese e-commerce platform specialised in clothing and sports apparel, aiming the increase in customer engagement, by providing a personalised experience with multiple types of recommendations across the platform. First, we address the reason why implementing recommender systems can benefit online platforms and the state of the art in that area. Then, a proposal and implementation of a customised system are presented, and its results discussed. © 2020, Springer Nature Switzerland AG.},

note = {cited By 2; Conference of 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference Date: 4 November 2020 Through 6 November 2020; Conference Code:251049},

keywords = {Data mining, Electronic commerce; Engines; Online systems; Recommender systems; User experience, Human evolution; Mining engines; Online platforms; Personalisation; State of the art},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DeBrito2019,

title = {Prediction of mortality and occurrence of complications for gastric cancer patients},

author = {M. A. De Brito and C. Neto and A. Abelha and J. MacHado},

editor = {Quintal F. Morgado-Dias F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075034483&doi=10.1109%2fCEAP.2019.8883494&partnerID=40&md5=47c8da4f5068b2f915c5edce9859a4dc},

doi = {10.1109/CEAP.2019.8883494},

isbn = {9781728129624},

year  = {2019},

date = {2019-01-01},

journal = {2019 International Conference on Engineering Applications, ICEA 2019 - Proceedings},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Gastric cancer is one of the most prevalent types of cancer in the whole world, affecting millions of people over the last decades. Its symptoms are ambiguous, which leads to late diagnoses, reducing the patients' chances of survival. In most countries, routine screenings are not usual, which also contributes to the detection of this gastric malignancy in later and more dangerous (and often fatal)stages. One of the main focus of improving healthcare services related to gastric cancer relies on increasing the survival rates. This and predicting if a patient will suffer from any complication following the surgery can aid the healthcare professionals in selecting better and more efficient treatment strategies. Thus, this constitutes as the aims of this study which will test and compare a set of classification models in order to improve the prediction accuracy. Data mining techniques will be put into use, since it's been proved they are one of the best ways of producing useful information for many businesses, including healthcare. © 2019 IEEE.},

note = {cited By 1; Conference of 2019 International Conference on Engineering Applications, ICEA 2019 ; Conference Date: 8 July 2019 Through 11 July 2019; Conference Code:153483},

keywords = {Classification (of information); Diagnosis; Diseases; Forecasting; Health care; Patient treatment, Classification models; complication occurrence; Efficient treatment; Gastric cancers; Health care professionals; Healthcare services; Mortality rate; Prediction accuracy, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cruz2019557,

title = {Application of data mining for the prediction of prophylactic measures in patients at risk of deep vein thrombosis},

author = {M. Cruz and M. Esteves and H. Peixoto and A. Abelha and J. Machado},

editor = {Reis L. P. Costanzo S. Adeli H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065104150&doi=10.1007%2f978-3-030-16187-3_54&partnerID=40&md5=d2faa227145ef872f4f15e8948021077},

doi = {10.1007/978-3-030-16187-3_54},

issn = {21945357},

year  = {2019},

date = {2019-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {932},

pages = {557-567},

publisher = {Springer Verlag},

abstract = {In the last decades, with the increase in the amount of data stored in the healthcare industry, it is also extended the possibility of obtaining important information to support the decision-making process of health professionals. This article has as evidence to apply Data Mining (DM) techniques to health databases of patients with medical Deep Vein Thrombosis (DVT) risk, with the objective of classifying, based on different attributes obtained in medical discharge reports, the main prophylactic measures taken. Therefore, to achieve this goal, the free software Weka was used aiming to facilitate the process of DM, along with the algorithms chosen. In view of this, it was concluded that the service to which each patient is associated is the most relevant factor for prophylactic measures followed by the age range to which the patient belongs. This study also deduces that it can be possible to obtain classifiers capable of predicting the best prophylactic measures with a qualitative level similar as one of a health professional and, thereafter, it can be possible to obtain the classification. © Springer Nature Switzerland AG 2019.},

note = {cited By 2; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},

keywords = {Blood vessels; Classification (of information); Decision making; Diseases; Forecasting; Health risks; Information systems; Information use; Medical computing; Risk assessment, Data mining, Decision making process; Deep vein thrombosis; Free software; Health professionals; Healthcare industry; Prophylactic measures; Weka},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Loreto2019568,

title = {Predicting low birth weight babies through data mining},

author = {P. Loreto and H. Peixoto and A. Abelha and J. Machado},

editor = {Adeli H. Rocha A. Costanzo S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065068373&doi=10.1007%2f978-3-030-16187-3_55&partnerID=40&md5=a44b920a5b272005f074e6603d35e795},

doi = {10.1007/978-3-030-16187-3_55},

issn = {21945357},

year  = {2019},

date = {2019-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {932},

pages = {568-577},

publisher = {Springer Verlag},

abstract = {Low Birth Weight (LBW) babies have a high risk of developing certain health conditions throughout their lives that affect negatively their quality of life. Therefore, a Decision Support System (DSS) that predicts whether a baby will be born with LBW would be of great interest. In this study, six different Data Mining (DM) algorithms are tested for five different scenarios. The scenarios combine information about the mother’s physical characteristics and habits, and the gestation. Results are promising and the best model achieved a sensitivity of 91,4% and a specificity of 99%. Good results were also achieved without considering the gestational age, which showed that the use of DM might be a good alternative to the traditional medical imaging exams in the prediction of LBW early in the pregnancy. © Springer Nature Switzerland AG 2019.},

note = {cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},

keywords = {Artificial intelligence; Classification (of information); Decision support systems; Health risks; Information systems; Information use; Medical imaging, CRISP-DM; Decision support system (dss); Gestational age; Health condition; Knowledge discovery in database; Low birth weights; Physical characteristics; Quality of life, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peixoto201937,

title = {Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining},

author = {H. Peixoto and A. Francisco and A. Duarte and M. Esteves and S. Oliveira and V. Lopes and A. Abelha and J. Machado},

editor = {Branco P. Portela C.F. Magalhaes L.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065038722&doi=10.1007%2f978-3-030-16447-8_4&partnerID=40&md5=86caef7589a1d51de058bdb33623a155},

doi = {10.1007/978-3-030-16447-8_4},

issn = {18678211},

year  = {2019},

date = {2019-01-01},

journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},

volume = {273},

pages = {37-46},

publisher = {Springer Verlag},

abstract = {Gastric cancer refers to the development of malign cells that can grow in any part of the stomach. With the vast amount of data being collected daily in healthcare environments, it is possible to develop new algorithms which can support the decision-making processes in gastric cancer patients treatment. This paper aims to predict, using the CRISP-DM methodology, the outcome from the hospitalization of gastric cancer patients who have undergone surgery, as well as the occurrence of postoperative complications during surgery. The study showed that, on one hand, the RF and NB algorithms are the best in the detection of an outcome of hospitalization, taking into account patients’ clinical data. On the other hand, the algorithms J48, RF, and NB offer better results in predicting postoperative complications. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019.},

note = {cited By 2; Conference of 10th International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2018 ; Conference Date: 21 November 2018 Through 23 November 2018; Conference Code:225079},

keywords = {Artificial intelligence; Decision making; Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Clinical data; Clinical decision support systems; CRISP-DM; Decision making process; Gastric cancers; Healthcare environments; Postoperative complications; WEKA, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peixoto2019558,

title = {Predicting Death and Morbidity in Perforated Peptic Ulcer},

author = {H. Peixoto and L. Correia e Silva and S. Pereira and T. Jesus and V. Lopes and A. Abelha and J. Machado},

editor = {Rocha A. Ferras C. Paredes M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061376631&doi=10.1007%2f978-3-030-11890-7_54&partnerID=40&md5=bcd18b79f24170a953648e6219eeee3f},

doi = {10.1007/978-3-030-11890-7_54},

issn = {21945357},

year  = {2019},

date = {2019-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {918},

pages = {558-568},

publisher = {Springer Verlag},

abstract = {Peptic ulcers are defined as defects in the gastrointestinal mucosa that extend through the muscularis mucosae. Although not being the most common complication, perforations stand out as being the complication with the highest mortality rate. To predict the probability of mortality, several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed. This article explores, using data mining in the medical data available, how the scoring systems perform when trying to predict mortality and patients’ state complication. We also try to conclude, from the two scoring systems presented, which predicts better the situations described. Regarding the results, we concluded that the PULP scoring allows a better mortality prediction achieving, in this case, above 90% accuracy, however, the results may be inconclusive due to the lack of patients who died in the dataset used. Regarding the complications, we concluded that, on the other hand, the Boey system achieves better results leading to a better prediction when it comes to predicting patients’ state complication. © 2019, Springer Nature Switzerland AG.},

note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2019 ; Conference Date: 6 February 2019 Through 8 February 2019; Conference Code:223499},

keywords = {Boey; Dataset; Death; Health complications; Scoring systems, Data mining, Diseases; Forecasting; Large dataset; Medical computing; Pulp},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Machado2019569,

title = {Predicting the Length of Hospital Stay After Surgery for Perforated Peptic Ulcer},

author = {J. Machado and A. C. Cardoso and I. Gomes and I. Silva and V. Lopes and H. Peixoto and A. Abelha},

editor = {Rocha A. Ferras C. Paredes M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061357086&doi=10.1007%2f978-3-030-11890-7_55&partnerID=40&md5=c2f374f26c35d638295694daa029c557},

doi = {10.1007/978-3-030-11890-7_55},

issn = {21945357},

year  = {2019},

date = {2019-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {918},

pages = {569-579},

publisher = {Springer Verlag},

abstract = {The management of peptic ulcer disease usually implies an urgent surgical procedure with the need of a patient’s hospital admission. By predicting the length of hospital stay of patients, improvements can be made regarding the quality of services provided to patients. This paper focuses on using real data to identify patterns in patients’ profiles and surgical events, in order to predict if patients will need hospital care for a shorter or longer period of time. This goal is pursued using a Data Mining process which follows the CRISP-DM methodology. In particular, classification models are built by combining different scenarios, algorithms and sampling methods. The data mining model which performed best achieved an accuracy of 87.30%, a specificity of 89.40%, and a sensitivity of 81.30%, using JRip, a rule-based algorithm and Cross Validation as a sampling method. © 2019, Springer Nature Switzerland AG.},

note = {cited By 1; Conference of International Conference on Information Technology and Systems, ICITS 2019 ; Conference Date: 6 February 2019 Through 8 February 2019; Conference Code:223499},

keywords = {Artificial intelligence; Classification (of information); Decision support systems; Diseases; Forecasting; Hospitals; Surgery, Classification models; CRISP-DM; Data mining process; Length of hospital stays; Peptic ulcer disease; Peptic ulcers; Rule based algorithms; Surgical procedures, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ferreira2018137,

title = {Predictive data mining in nutrition therapy},

author = {D. Ferreira and H. Peixoto and J. Machado and A. Abelha},

editor = {Gil P. Henriques J. Teixeira C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057334712&doi=10.1109%2fCONTROLO.2018.8516413&partnerID=40&md5=dee7234bc455a021e666a52be4dbb1fe},

doi = {10.1109/CONTROLO.2018.8516413},

isbn = {9781538653463},

year  = {2018},

date = {2018-01-01},

journal = {13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings},

pages = {137-142},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The assessment and measurement of health status in communities throughput the world is a massive information technology challenge. Data mining, plays a vital role in health care industry since it really has the potential to generate a knowledge-rich environment that reduces medical errors, decreases costs by increasing efficiency, improves the quality of clinical decisions and significantly enhances patient's outcomes and quality of life. This study falls within the context of nutrition evaluation and its main goal is to apply classification algorithms in order to predict if a patient needs to be followed by a nutrition specialist. One of the tools resorted in this study was the Waikato Environment for Knowledge Analysis (Weka in advance) Workbench since it allows to quickly try out and compare different machine learning solutions. The tasks involved in the development of this project included data preparation, data preprocessing, data transformation and cleaning, application of several classifiers and its respective evaluation through performance measures that include the confusion matrix, accuracy, error rate, and others. The accomplished results showed to be quite optimistic presenting promising values of performance measures. specifically an accuracy around 91 %. © 2018 IEEE.},

note = {cited By 9; Conference of 13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 ; Conference Date: 4 June 2018 Through 6 June 2018; Conference Code:141782},

keywords = {Artificial intelligence; Classification (of information); Health care; Information technology; Learning systems; Linear transformations; Medical computing; Metadata; Nutrition; Soft computing, Classification algorithm; Clinical decision; Confusion matrices; Data transformation; Healthcare industry; Knowledge analysis; Performance measure; Predictive data mining, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silva2018516,

title = {Data mining for prediction of length of stay of cardiovascular accident inpatients},

author = {C. Silva and D. Oliveira and H. Peixoto and J. Machado and A. Abelha},

editor = {Alexandrov D. A. Chugunov A.V. Boukhanovsky A.V.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057139340&doi=10.1007%2f978-3-030-02843-5_43&partnerID=40&md5=f8bd9dee0c6500c60b42f061fb2af9c3},

doi = {10.1007/978-3-030-02843-5_43},

issn = {18650929},

year  = {2018},

date = {2018-01-01},

journal = {Communications in Computer and Information Science},

volume = {858},

pages = {516-527},

publisher = {Springer Verlag},

abstract = {The healthcare sector generates large amounts of data on a daily basis. This data holds valuable knowledge that, beyond supporting a wide range of medical and healthcare functions such as clinical decision support, can be used for improving profits and cutting down on wasted overhead. The evaluation and analysis of stored clinical data may lead to the discovery of trends and patterns that can significantly enhance overall understanding of disease progression and clinical management. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a data mining project approach to predict the hospitalization period of cardiovascular accident patients. This provides an effective tool for the hospital cost containment and management efficiency. The data used for this project contains information about patients hospitalized in Cardiovascular Accident’s unit in 2016 for having suffered a stroke. The Weka software was used as the machine learning toolkit. © Springer Nature Switzerland AG 2018.},

note = {cited By 5; Conference of 3rd International Conference on Digital Transformation and Global Society, DTGS 2018 ; Conference Date: 30 May 2018 Through 2 June 2018; Conference Code:220939},

keywords = {Accidents; Decision support systems; Forecasting; Health care; Hospitals; Learning systems, Clinical decision support; Clinical management; Disease progression; Evaluation and analysis; Healthcare sectors; Large amounts of data; Management efficiency; Weka, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neto20171104,

title = {Knowledge Discovery from Surgical Waiting lists},

author = {C. Neto and H. Peixoto and V. Abelha and A. Abelha and J. Machado},

editor = {Peppard J. Varajao J.E. Cruz-Cunha M.M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040217774&doi=10.1016%2fj.procs.2017.11.141&partnerID=40&md5=f1a8b6d40ae079b7d7e26aef6b8e7e7c},

doi = {10.1016/j.procs.2017.11.141},

issn = {18770509},

year  = {2017},

date = {2017-01-01},

journal = {Procedia Computer Science},

volume = {121},

pages = {1104-1111},

publisher = {Elsevier B.V.},

abstract = {Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Data mining is the process of pattern discovery and extraction where huge amount of data is involved. Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. In this work is presented an approach for the use of data mining in the context of waiting lists for surgery, namely for predicting the type of surgery (programmed or additional) for a record in the list. © 2017 The Authors. Published by Elsevier B.V.},

note = {cited By 9; Conference of International Conference on ENTERprise Information Systems, CENTERIS 2017, International Conference on Project MANagement, ProjMAN 2017 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2017 ; Conference Date: 8 November 2017 Through 10 November 2017; Conference Code:133143},

keywords = {Artificial intelligence; Classification (of information); Decision support systems; Extraction; Health care; Information management; Information systems; Knowledge representation; Project management; Surgery, Data collection; Data mining applications; Healthcare industry; Knowledge discovery in data basis; Knowledge discovery in database; Pattern discovery; Surgical waiting lists; Waiting lists, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rodrigues2017591,

title = {Understanding Stroke in Dialysis and Chronic Kidney Disease},

author = {M. Rodrigues and H. Peixoto and M. Esteves and J. Machado and Abelha},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033482094&doi=10.1016%2fj.procs.2017.08.296&partnerID=40&md5=7da878098027c0cfd48debf22d157457},

doi = {10.1016/j.procs.2017.08.296},

issn = {18770509},

year  = {2017},

date = {2017-01-01},

journal = {Procedia Computer Science},

volume = {113},

pages = {591-596},

publisher = {Elsevier B.V.},

abstract = {Patients with severe kidney failure need to be carefully monitored. One of the many treatments is called Continuous Ambulatory Peritoneal Dialysis (CAPD). This kind of treatment intends to maintain the blood tests as normal as possible. Data Mining and Machine Learning can take a simple and meaningless blood's test data set and build it into a Decision Support System. Through this article, Machine Learning algorithms will be explored with different Data Mining Models in order to extract knowledge and classify a patient with a stroke risk or not, according to their blood analysis. Peer-review under responsibility of the Conference Program Chairs. © 2017 The Authors. Published by Elsevier B.V.},

note = {cited By 11; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912},

keywords = {Artificial intelligence; Blood; Classification (of information); Data communication systems; Decision support systems; Dialysis; Health care; Learning algorithms; Learning systems; Risk assessment; Statistical tests, Blood analysis; Blood test; Chronic kidney disease; Conference programs; Data mining models; Peer review; Peritoneal dialysis; Test data, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Fonseca2017565,

title = {Step Towards Prediction of Perineal Tear},

author = {F. Fonseca and H. Peixoto and F. Miranda and J. Machado and A. Abelha},

editor = {Shakshuki E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033453791&doi=10.1016%2fj.procs.2017.08.284&partnerID=40&md5=48288649c62db1ddede9cdc0711b5b5d},

doi = {10.1016/j.procs.2017.08.284},

issn = {18770509},

year  = {2017},

date = {2017-01-01},

journal = {Procedia Computer Science},

volume = {113},

pages = {565-570},

publisher = {Elsevier B.V.},

abstract = {The aim of this study is to predict, through data mining tools, the incidence of perineal tear. This kind of laceration developed during child delivery might imply surgery and entails a set of several consequences. Clinical Decision Support Systems, with the information collected from patients' electronic health records combined with the data mining techniques, may decrease the incidence of perineal tears during labour. Peer-review under responsibility of the Conference Program Chairs. © 2017 The Authors. Published by Elsevier B.V.},

note = {cited By 6; Conference of 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2017 and the 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2017 ; Conference Date: 18 September 2017 Through 20 September 2017; Conference Code:130912},

keywords = {Artificial intelligence; Data communication systems; Decision support systems; Health care; Obstetrics, Clinical decision support systems; Conference programs; Data-mining tools; Electronic health record; Peer review; Perineal Tear, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silva2017153,

title = {Text mining models to predict brain deaths using X-Rays clinical notes},

author = {A. Silva and F. Portela and M. F. Santos and J. Machado and A. Abelha},

editor = {Gelbukh A. Prasath R.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018394209&doi=10.1007%2f978-3-319-58130-9_15&partnerID=40&md5=211a0c8c61a17d7a26286f64f21c5dbe},

doi = {10.1007/978-3-319-58130-9_15},

issn = {03029743},

year  = {2017},

date = {2017-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {10089 LNAI},

pages = {153-163},

publisher = {Springer Verlag},

abstract = {The prediction of events is a task associated to the Data Science area. In the health, this method is extremely useful to predict critical events that may occur in people, or in a specific area. The Text Mining is a technique that consists in retrieving information from text files. In the Medical Field, the Data Mining and Text Mining solutions can help to prevent the occurrence of certain events to a patient. This project involves the use of Text Mining to predict the Brain Death by using the X-Ray clinical notes. This project is creating reliable predictive models with non-structured text. This project was developed using real data provided by Centro Hospitalar do Porto. The results achieved are very good reaching a sensitivity of 98% and a specificity of 88%. © 2017, Springer International Publishing AG.},

note = {cited By 2; Conference of 4th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2016 ; Conference Date: 13 November 2016 Through 19 November 2016; Conference Code:191459},

keywords = {Brain death; Clinical notes; Critical events; Medical fields; Predictive models; Specific areas; Structured text; Text mining, Data mining, Forecasting; Medical computing; X rays},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silva201645,

title = {Towards of automatically detecting brain death patterns through text mining},

author = {A. Silva and F. Portela and M. F. Santos and J. Machado and A. Abelha},

editor = {Huemer C. Poels G. Kornyshova E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010380045&doi=10.1109%2fCBI.2016.49&partnerID=40&md5=c823dfb35568399d22d13af6c9e76461},

doi = {10.1109/CBI.2016.49},

isbn = {9781509032310},

year  = {2016},

date = {2016-01-01},

journal = {Proceedings - CBI 2016: 18th IEEE Conference on Business Informatics},

volume = {2},

pages = {45-52},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the area of medicine, x-rays are very useful to check if the patient suffers from brain death. Their diagnosis is made using free text. This type of record difficult the process of making qualitative analysis in order to automatically detect possible brain problems. This project aims to make qualitatively and quantitatively analysis of Brain Computed Tomography (CT) diagnosis using text analysis tools as is Natural Language Processing and Text Mining. In this work a set of related words that can means patterns in CT reports was detected. The dataset was provided by the Centro Hospitalar do Porto-Hospital de Santo António and it contains information about patient deaths and CT done to the brain. With the analysis made, a new research and analysis perspectives of structured and unstructured texts in this field was opened. © 2016 IEEE.},

note = {cited By 2; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364},

keywords = {Brain death; NAtural language processing; Qualitative analysis; Related word; Research and analysis; Text analysis; Text mining; Unstructured texts, Computerized tomography; Diagnosis; Information science; Natural language processing systems; X rays, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pereira2016579,

title = {Predicting triage waiting time in maternity emergency care by means of data mining},

author = {S. Pereira and L. Torres and F. Portela and M. F. Santos and J. Machado and A. Abelha},

editor = {Reis L. P. Adeli H. Rocha A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961639111&doi=10.1007%2f978-3-319-31307-8_60&partnerID=40&md5=f3949e7b9e5bc5b1211b23e76d69bde2},

doi = {10.1007/978-3-319-31307-8_60},

issn = {21945357},

year  = {2016},

date = {2016-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {445},

pages = {579-588},

publisher = {Springer Verlag},

abstract = {Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times. © Springer International Publishing Switzerland 2016.},

note = {cited By 5; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},

keywords = {Adverse events; Emergency care; Healthcare organizations; Prediction model; Waiting-time, Artificial intelligence; Decision support systems; Embedded systems; Forecasting; Information systems, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Braga201615,

title = {Real-time models to predict the use of vasopressors in monitored patients},

author = {A. Braga and F. Portela and M. F. Santos and A. Abelha and J. Machado and Á. Silva and F. Rua},

editor = {Zheng X. Zeng D.D. Chen H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958580639&doi=10.1007%2f978-3-319-29175-8_2&partnerID=40&md5=4cd41eed311b3d67d7a3dfef7fa8c2d3},

doi = {10.1007/978-3-319-29175-8_2},

issn = {03029743},

year  = {2016},

date = {2016-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9545},

pages = {15-25},

publisher = {Springer Verlag},

abstract = {The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors. © Springer International Publishing Switzerland 2016.},

note = {cited By 0; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109},

keywords = {Data mining, Data mining models; Decision making process; INTCare; Laboratory analysis; Real time; Real-time data mining; Vasopressors; Vital sign, Decision making; Patient treatment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pereira2016105,

title = {Predicting pre-triage waiting time in a maternity emergency room through data mining},

author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},

editor = {Zheng X. Zeng D.D. Chen H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958528894&doi=10.1007%2f978-3-319-29175-8_10&partnerID=40&md5=2950ba905955e8200d582703152d613b},

doi = {10.1007/978-3-319-29175-8_10},

issn = {03029743},

year  = {2016},

date = {2016-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9545},

pages = {105-117},

publisher = {Springer Verlag},

abstract = {An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The implementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services. © Springer International Publishing Switzerland 2016.},

note = {cited By 5; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109},

keywords = {Adverse events; Business Intelligence platform; Classification algorithm; Emergency care; IDSS; Information systems and technologies; Maternity care; Triage system, Artificial intelligence; Decision support systems; Emergency rooms; Forecasting; Gynecology; Health; Interoperability; Obstetrics, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Oliveira2015626,

title = {Intelligent Decision Support to Predict Patient Barotrauma Risk in Intensive Care Units},

author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},

editor = {Cruz-Cunha M. M. Eduardo V.J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962786672&doi=10.1016%2fj.procs.2015.08.576&partnerID=40&md5=9da1a81f449b781b145fd534e71f6c96},

doi = {10.1016/j.procs.2015.08.576},

issn = {18770509},

year  = {2015},

date = {2015-01-01},

journal = {Procedia Computer Science},

volume = {64},

pages = {626-634},

publisher = {Elsevier B.V.},

abstract = {The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: "risk" and "no risk". Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated. © 2015 The Authors. Published by Elsevier B.V.},

note = {cited By 7; Conference of Conference on ENTERprise Information Systems/International Conference on Project MANagement/Conference on Health and Social Care Information Systems and Technologies, CENTERIS 2015 ; Conference Date: 7 October 2015 Through 9 October 2015; Conference Code:123098},

keywords = {Barotrauma; Decision supports; INTCare; Intensive care; Mechanical ventilation; Patient-centered, Data mining, Decision support systems; Decision trees; Forecasting; Health; Information management; Information systems; Intensive care units; Probability; Project management; Risks; Ventilation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela201577,

title = {Real-time decision support using data mining to predict blood pressure critical events in intensive medicine patients},

author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and F. Rua and Á. Silva},

editor = {Hervas R. Bravo J. Villarreal V.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954110962&doi=10.1007%2f978-3-319-26508-7_8&partnerID=40&md5=4b2b9925c5d4064a973f40f5a4636dff},

doi = {10.1007/978-3-319-26508-7_8},

issn = {03029743},

year  = {2015},

date = {2015-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9456},

pages = {77-90},

publisher = {Springer Verlag},

abstract = {Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%. © Springer International Publishing Switzerland 2015.},

note = {cited By 13; Conference of 1st International Conference on Ambient Intelligence for Health, AmIHEALTH 2015 ; Conference Date: 1 December 2015 Through 4 December 2015; Conference Code:159599},

keywords = {Artificial intelligence; Blood pressure; Decision support systems; Hospital data processing; Intensive care units, Continuous monitoring; Critical events; Decision supports; Intcare; Mining classification; Patient condition; Real time; Real time decisions, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Oliveira2015122,

title = {Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables},

author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva and F. Rua},

editor = {Costa E. Machado P. Pereira F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945970524&doi=10.1007%2f978-3-319-23485-4_13&partnerID=40&md5=8d9e4358d2a397a7ddba9da8359d63bf},

doi = {10.1007/978-3-319-23485-4_13},

issn = {03029743},

year  = {2015},

date = {2015-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9273},

pages = {122-127},

publisher = {Springer Verlag},

abstract = {Predicting barotrauma occurrence in intensive care patients is a difficult task. Data Mining modelling can contribute significantly to the identification of patients who will suffer barotrauma. This can be achieved by grouping patient data, considering a set of variables collected from ventilators directly related with barotrauma, and identifying similarities among them. For clustering have been considered k-means and k-medoids algortihms (Partitioning Around Medoids). The best model induced presented a Davies-Bouldin Index of 0.64. This model identifies the variables that have more similarity among the variables monitored by the ventilators and the occurrence of barotrauma. © Springer International Publishing Switzerland 2015.},

note = {cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439},

keywords = {Artificial intelligence; Correlation methods; Hospital data processing; Intensive care units, Barotrauma; Clustering; Davies-Bouldin index; Intensive-care patients; Partitioning around medoids; Patient data; Plateau pressures; Similarity, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pereira2015116,

title = {Predicting preterm birth in maternity care by means of data mining},

author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},

editor = {Costa E. Machado P. Pereira F.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945926609&doi=10.1007%2f978-3-319-23485-4_12&partnerID=40&md5=49364e9e0ed601bbcecbef6c655affb4},

doi = {10.1007/978-3-319-23485-4_12},

issn = {03029743},

year  = {2015},

date = {2015-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9273},

pages = {116-121},

publisher = {Springer Verlag},

abstract = {Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively. © Springer International Publishing Switzerland 2015.},

note = {cited By 1; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439},

keywords = {Artificial intelligence; Decision making; Forecasting; Obstetrics, Data mining, Data mining models; Decision making process; Maternity care; Preterm birth; Preterm deliveries; Real data; Real environments; Sensitivity and specificity},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Brandão2015594,

title = {Predicting the risk associated to pregnancy using Data Mining},

author = {A. Brandão and E. Pereira and F. Portela and M. Santos and A. Abelha and J. Machado},

editor = {Filipe J. Filipe J. Loiseau S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943227689&doi=10.5220%2f0005286805940601&partnerID=40&md5=1a23bacaf6154af6a890cd1fcc2bfafa},

doi = {10.5220/0005286805940601},

isbn = {9789897580741},

year  = {2015},

date = {2015-01-01},

journal = {ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings},

volume = {2},

pages = {594-601},

publisher = {SciTePress},

abstract = {Woman willing to terminate pregnancy should in general use a specialized health unit, as it is the case of Maternidade Júlio Dinis in Porto, Portugal. One of the four stages comprising the process is evaluation. The purpose of this article is to evaluate the process of Voluntary Termination of Pregnancy and, consequently, identify the risk associated to the patients. Data Mining (DM) models were induced to predict the risk in a real environment. Three different techniques were considered: Decision Tree (DT), Support Vector Machine (SVM) and Generalized Linear Models (GLM) to perform the classification task. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied to drive this work. Very promising results were obtained, achieving a sensitivity of approximately 93%.},

note = {cited By 12; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667},

keywords = {Artificial intelligence; Competitive intelligence; Decision support systems; Decision trees; Digital storage; Obstetrics; Support vector machines, Classification tasks; Data mining models; Generalized linear model; Intelligent decision support systems; Real environments; Technology acceptance; Three different techniques; Voluntary interruption of pregnancy, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela2014165,

title = {Preventing patient cardiac arrhythmias by using data mining techniques},

author = {F. Portela and M. Filipe Santos and A. Silva and F. Rua and A. Abelha and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925666219&doi=10.1109%2fIECBES.2014.7047478&partnerID=40&md5=251834d68395814ab7a32a47dfef6a59},

doi = {10.1109/IECBES.2014.7047478},

isbn = {9781479940844},

year  = {2014},

date = {2014-01-01},

journal = {IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet"},

pages = {165-170},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Cardiac Arrhythmia (CA) is very dangerous and can significantly undermine patient condition. New tools are fundamental to forecast and to prevent possible critical situations. In order to help clinicians acting proactively, predictive data mining real-time models were induced using online-learning. As input variables were considered those acquired at the patient admission and complementary variables (vital signs, laboratory results, therapeutics) hourly collected. The results are very motivating; sensitivity near to 95% was obtained when using Support Vector Machines. The approach explored in this work reveals to be an interesting contribution to the healthcare in terms of predicting CA and a good direction to be further explored. © 2014 IEEE.},

note = {cited By 18; Conference of 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014 ; Conference Date: 8 December 2014 Through 10 December 2014; Conference Code:111205},

keywords = {Biomedical engineering; Diseases, Cardiac arrhythmia; Input variables; Online learning; Patient admissions; Patient condition; Predictive data mining; Real-time models; Vital sign, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela2013410,

title = {Pervasive Ensemble Data Mining Models to Predict Organ Failure and Patient Outcome in Intensive Medicine},

author = {F. Portela and M. F. Santos and Á. Silva and A. Abelha and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904295892&doi=10.1007%2f978-3-642-54105-6_27&partnerID=40&md5=b95b894d9b73bbb6bdc192f7fe27359f},

doi = {10.1007/978-3-642-54105-6_27},

issn = {18650929},

year  = {2013},

date = {2013-01-01},

journal = {Communications in Computer and Information Science},

volume = {415},

pages = {410-425},

publisher = {Springer Verlag},

address = {Barcelona},

abstract = {The number of patients admitted to Intensive Care Units with organ failure is significant. This type of situation is very common in Intensive Medicine. Intensive medicine is a specific area of medicine whose purpose is to avoid organ failure and recover patients in weak conditions. This type of problems can culminate in the death of patient. In order to help the intensive medicine professionals at the exact moment of decision making, a Pervasive Intelligent Decision Support System called INTCare was developed. INTCare uses ensemble data mining to predict the probability of occurring an organ failure or patient death for the next hour. To assure the better results, a measure was implemented to assess the models quality. The transforming process and model induction are both performed automatically and in real-time. The ensemble uses online-learning to improve the models. This paper explores the ensemble approach to improve the decision process in intensive Medicine. © Springer-Verlag Berlin Heidelberg 2013.},

note = {cited By 1; Conference of 4th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2012 ; Conference Date: 4 October 2012 Through 7 October 2012; Conference Code:106350},

keywords = {Data mining, Decision support systems; Intensive care units; Knowledge engineering; Knowledge management, Ensemble; INTCare; Intensive care; Organ failure; Patient Outcome; Pervasive healthcare; Real-time},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gonçalves2013201,

title = {Predict sepsis level in intensive medicine - Data mining approach},

author = {J. M. C. Gonçalves and F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876221428&doi=10.1007%2f978-3-642-36981-0_19&partnerID=40&md5=d5ca7bbcda25f0d1f2d0a30850f63245},

doi = {10.1007/978-3-642-36981-0_19},

issn = {21945357},

year  = {2013},

date = {2013-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {206 AISC},

pages = {201-211},

publisher = {Springer Verlag},

address = {Olhao, Algarve},

abstract = {This paper aims to support doctor's decision-making on predicting the Sepsis level. Thus, a set of Data Mining (DM) models were developed using prevision techniques and classification models. These models enable a better doctor's decision having into account the Sepsis level of the patient. The DM models use real data collected from the Intensive Care Unit of the Santo António Hospital, in Oporto, Portugal. Classification DM models were considered to predict sepsis level in a supervised learning approach. The models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. The models were assessed using the Confusion Matrix, associated metrics, and Cross-validation. The analysis of the total error rate, sensitivity, specificity and accuracy were the metrics used to identify the most relevant measures to predict sepsis level. This work demonstrates that it is possible to predict with great accuracy the sepsis level. © 2013 Springer-Verlag.},

note = {cited By 12; Conference of 2013 World Conference on Information Systems and Technologies, WorldCIST 2013 ; Conference Date: 27 March 2013 Through 30 March 2013; Conference Code:96582},

keywords = {Classification (of information); Decision making; Decision trees; Forecasting; Information systems; Intensive care units, Classification models; Confusion matrices; Data mining models; INTCare; Intensive care; Sepsis; Supervised learning approaches; Total error rates, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela2011419,

title = {Enabling real-time intelligent decision support in intensive care},

author = {F. Portela and M. F. Santos and P. Gago and A. Silva and F. Rua and A. Abelha and J. Machado and J. Neves},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898952827&partnerID=40&md5=c145f0e0582ad54ee762733fb006d977},

isbn = {9789077381663},

year  = {2011},

date = {2011-01-01},

journal = {ESM 2011 - 2011 European Simulation and Modelling Conference: Modelling and Simulation 2011},

pages = {419-426},

publisher = {EUROSIS},

address = {Guimaraes},

abstract = {Medical devices in ICU allow for both continuous monitoring of patients and data collection. Nevertheless, the amount of data to be considered is such that it is difficult for doctors to extract all the useful knowledge. In order to help uncover some of that knowledge we have built an IDSS based in the agent's paradigm and using data mining techniques to build prediction models. With the intention of collecting as much data as possible the data acquisition process was automated. Furthermore, given the paramount importance of data quality for data mining a data quality agent responsible for detecting the errors in the data was devised. Indeed, data acquisition in the ICU is error prone as, for instance, sensors may be displaced as patients move. The aim of this paper is to present the overall KDD process implemented, presenting in detail the data transformations that were done and the benefits achieved. ©2011 EUROSIS-ETI.},

note = {cited By 15; Conference of 25th European Simulation and Modelling Conference, ESM 2011 ; Conference Date: 24 October 2011 Through 26 October 2011; Conference Code:104378},

keywords = {Acquisition process; Continuous monitoring; Data engineering; Data transformation; Intelligent decision support; KDD; Prediction model; Real-time, Agents; Biomedical equipment; Data acquisition; Decision support systems; Intelligent agents; Intensive care units; Modal analysis, Data mining},

pubstate = {published},

tppubtype = {inproceedings}

}