2022
Sousa, R.; Oliveira, D.; Durães, D.; Neto, C.; Machado, J.
Medical Recommendation System Based on Daily Clinical Reports: A Proposed NLP Approach for Emergency Departments Proceedings Article
Em: M., Stahl F. Bramer (Ed.): pp. 315-320, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 03029743, (cited By 0; Conference of 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 ; Conference Date: 13 December 2022 Through 15 December 2022; Conference Code:287589).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Diagnosis; Emergency rooms; Natural language processing systems; Recommender systems, Decision support systems, Emergency departments; Emergency health department; Global impacts; Hospital administration; Language processing; Natural language processing; Natural languages; Operational management; Text-mining; Unstructured data
@inproceedings{Sousa2022315,
title = {Medical Recommendation System Based on Daily Clinical Reports: A Proposed NLP Approach for Emergency Departments},
author = {R. Sousa and D. Oliveira and D. Durães and C. Neto and J. Machado},
editor = {Stahl F. Bramer M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144818897&doi=10.1007%2f978-3-031-21441-7_24&partnerID=40&md5=c46b6c78b09ded01e7ecd348b5a7dea6},
doi = {10.1007/978-3-031-21441-7_24},
issn = {03029743},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13652 LNAI},
pages = {315-320},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The operational management of an emergency department (ED) requires more attention from hospital administration since it can have a global impact on the institution’s management, increasing the probability of adverse events and worsening hospital expenses. Effective management of an ED potentially results in fewer hospitalisations after an ED admission. The purpose of the present study is to perform a multi-class prediction based on: a) structured data and unstructured data in an ED episode; and b) unstructured data generated during the inpatient event, just after the ED episode. The designed prediction model will lay the foundation for an ED Decision Support System based on symptoms and principal diagnoses. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 ; Conference Date: 13 December 2022 Through 15 December 2022; Conference Code:287589},
keywords = {Artificial intelligence; Diagnosis; Emergency rooms; Natural language processing systems; Recommender systems, Decision support systems, Emergency departments; Emergency health department; Global impacts; Hospital administration; Language processing; Natural language processing; Natural languages; Operational management; Text-mining; Unstructured data},
pubstate = {published},
tppubtype = {inproceedings}
}
The operational management of an emergency department (ED) requires more attention from hospital administration since it can have a global impact on the institution’s management, increasing the probability of adverse events and worsening hospital expenses. Effective management of an ED potentially results in fewer hospitalisations after an ED admission. The purpose of the present study is to perform a multi-class prediction based on: a) structured data and unstructured data in an ED episode; and b) unstructured data generated during the inpatient event, just after the ED episode. The designed prediction model will lay the foundation for an ED Decision Support System based on symptoms and principal diagnoses. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Oliveira, C.; Sousa, R.; Peixoto, H.; Machado, J.
Improving the Effectiveness of Heart Disease Diagnosis with Machine Learning Proceedings Article
Em: A., Fernandez A. Almeida A. Gonzalez-Briones (Ed.): pp. 222-231, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 18650929, (cited By 1; Conference of 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:285119).
Resumo | Links | BibTeX | Etiquetas: Cardiology; Classification (of information); Clinical research; Data mining; Decision trees; Diagnosis; Diseases; Health risks; Heart; Machine learning; Optimization, Causes of death; Condition; Data mining methods; Data-mining tools; Health records; Heart disease; Heart disease diagnosis; Machine-learning; Medical teams; Patient information, Decision support systems
@inproceedings{Oliveira2022222,
title = {Improving the Effectiveness of Heart Disease Diagnosis with Machine Learning},
author = {C. Oliveira and R. Sousa and H. Peixoto and J. Machado},
editor = {Fernandez A. Almeida A. Gonzalez-Briones A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141686201&doi=10.1007%2f978-3-031-18697-4_18&partnerID=40&md5=5b5ddaa362353509a172400b23b54b71},
doi = {10.1007/978-3-031-18697-4_18},
issn = {18650929},
year = {2022},
date = {2022-01-01},
journal = {Communications in Computer and Information Science},
volume = {1678 CCIS},
pages = {222-231},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Despite technological and clinical improvements, heart disease remains one of the leading causes of death worldwide. A significant shift in the paradigm would be for medical teams to be able to accurately identify, at an early stage, whether a patient is at risk of developing or having heart disease, using data from their health records paired with Data Mining tools. As a result, the goal of this research is to determine whether a patient has a cardiac condition by using Data Mining methods and patient information to aid in the construction of a Clinical Decision Support System. With this purpose, we use the CRISP-DM technique to try to forecast the occurrence of cardiac disorders. The greatest results were obtained utilizing the Random Forest technique and the Percentage Split sampling method with a 66% training rate. Other approaches, such as Naïve Bayes, J48, and Sequential Minimal Optimization, also produced excellent results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 1; Conference of 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022 ; Conference Date: 13 July 2022 Through 15 July 2022; Conference Code:285119},
keywords = {Cardiology; Classification (of information); Clinical research; Data mining; Decision trees; Diagnosis; Diseases; Health risks; Heart; Machine learning; Optimization, Causes of death; Condition; Data mining methods; Data-mining tools; Health records; Heart disease; Heart disease diagnosis; Machine-learning; Medical teams; Patient information, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite technological and clinical improvements, heart disease remains one of the leading causes of death worldwide. A significant shift in the paradigm would be for medical teams to be able to accurately identify, at an early stage, whether a patient is at risk of developing or having heart disease, using data from their health records paired with Data Mining tools. As a result, the goal of this research is to determine whether a patient has a cardiac condition by using Data Mining methods and patient information to aid in the construction of a Clinical Decision Support System. With this purpose, we use the CRISP-DM technique to try to forecast the occurrence of cardiac disorders. The greatest results were obtained utilizing the Random Forest technique and the Percentage Split sampling method with a 66% training rate. Other approaches, such as Naïve Bayes, J48, and Sequential Minimal Optimization, also produced excellent results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
2016
Ribeiro, A.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.; Rua, F.
Patients' admissions in intensive care units: A clustering overview Proceedings Article
Em: E., Huemer C. Poels G. Kornyshova (Ed.): pp. 38-44, Institute of Electrical and Electronics Engineers Inc., 2016, ISBN: 9781509032310, (cited By 0; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364).
Resumo | Links | BibTeX | Etiquetas: admissions; clustering; Clustering techniques; Davies-Bouldin index; Health care professionals; INTCare; Intensive care, Artificial intelligence; Data mining; Information science; Intensive care units, Decision support systems
@inproceedings{Ribeiro201638,
title = {Patients' admissions in intensive care units: A clustering overview},
author = {A. Ribeiro and F. Portela and M. F. Santos and J. Machado and A. Abelha and F. Rua},
editor = {Huemer C. Poels G. Kornyshova E.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010284769&doi=10.1109%2fCBI.2016.48&partnerID=40&md5=0578138e0e8a81352aed1d0cd29a6ccf},
doi = {10.1109/CBI.2016.48},
isbn = {9781509032310},
year = {2016},
date = {2016-01-01},
journal = {Proceedings - CBI 2016: 18th IEEE Conference on Business Informatics},
volume = {2},
pages = {38-44},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Intensive Care is one of the most critical areas ofmedicine. Its multidisciplinary nature makes it a very wide area, requiring all types of healthcare professionals. Given the criticalenvironment of intensive care units, it becomes evident the need touse technology of decision support systems to improve healthcareservices and Intensive Care Units management. By discovering thecommon characteristics of the admitted patients it is possible toimprove these outcomes. In this study clustering techniques wereapplied to data collected from admitted patients in Intensive CareUnit. The best results presented a Silhouette of 1, with a distance tocentroids of 6.2e-17 and a Davies-Bouldin index of -0.652. © 2016 IEEE.},
note = {cited By 0; Conference of 18th IEEE Conference on Business Informatics, CBI 2016 ; Conference Date: 29 August 2016 Through 1 September 2016; Conference Code:125364},
keywords = {admissions; clustering; Clustering techniques; Davies-Bouldin index; Health care professionals; INTCare; Intensive care, Artificial intelligence; Data mining; Information science; Intensive care units, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Intensive Care is one of the most critical areas ofmedicine. Its multidisciplinary nature makes it a very wide area, requiring all types of healthcare professionals. Given the criticalenvironment of intensive care units, it becomes evident the need touse technology of decision support systems to improve healthcareservices and Intensive Care Units management. By discovering thecommon characteristics of the admitted patients it is possible toimprove these outcomes. In this study clustering techniques wereapplied to data collected from admitted patients in Intensive CareUnit. The best results presented a Silhouette of 1, with a distance tocentroids of 6.2e-17 and a Davies-Bouldin index of -0.652. © 2016 IEEE.
2015
Veloso, R.; Portela, F.; Santos, M. F.; Silva, Á.; Rua, F.; Abelha, A.; Machado, J.
Using domain knowledge to improve intelligent decision support in intensive medicine: A study of bacteriological infections Proceedings Article
Em: S., Filipe J. Filipe J. Loiseau (Ed.): pp. 582-587, SciTePress, 2015, ISBN: 9789897580741, (cited By 2; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667).
Resumo | Links | BibTeX | Etiquetas: Antibiotics; Artificial intelligence; Bacteria; Intelligent agents; Intensive care units, Decision support systems, Decision supports; Heuristics; Infections; Intcare; Therapies
@inproceedings{Veloso2015582,
title = {Using domain knowledge to improve intelligent decision support in intensive medicine: A study of bacteriological infections},
author = {R. Veloso and F. Portela and M. F. Santos and Á. Silva and F. Rua and A. Abelha and J. Machado},
editor = {Filipe J. Filipe J. Loiseau S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943257423&doi=10.5220%2f0005286405820587&partnerID=40&md5=9915e188ba2224ad3a6278075d5747da},
doi = {10.5220/0005286405820587},
isbn = {9789897580741},
year = {2015},
date = {2015-01-01},
journal = {ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings},
volume = {2},
pages = {582-587},
publisher = {SciTePress},
abstract = {Nowadays antibiotic prescription is object of study in many countries. The rate of prescription varies from country to country, without being found the reasons that justify those variations. In intensive care units the number of new infections rising each day is caused by multiple factors like inpatient length of stay, low defences of the body, chirurgical infections, among others. In order to complement the support of the decision process about which should be the most efficient antibiotic it was developed a heuristic based in domain knowledge extracted from biomedical experts. This algorithm is implemented by intelligent agents. When an alert appear on the presence of a new infection, an agent collects the microbiological results for cultures, it permits to identify the bacteria, then using the rules it searches for a role of antibiotics that can be administered to the patient, based on past results. At the end the agent presents to physicians the top-five sets and the success percentage of each antibiotic. This paper presents the approach proposed and a test with a particular bacterium using real data provided by an Intensive Care Unit.},
note = {cited By 2; 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 = {Antibiotics; Artificial intelligence; Bacteria; Intelligent agents; Intensive care units, Decision support systems, Decision supports; Heuristics; Infections; Intcare; Therapies},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays antibiotic prescription is object of study in many countries. The rate of prescription varies from country to country, without being found the reasons that justify those variations. In intensive care units the number of new infections rising each day is caused by multiple factors like inpatient length of stay, low defences of the body, chirurgical infections, among others. In order to complement the support of the decision process about which should be the most efficient antibiotic it was developed a heuristic based in domain knowledge extracted from biomedical experts. This algorithm is implemented by intelligent agents. When an alert appear on the presence of a new infection, an agent collects the microbiological results for cultures, it permits to identify the bacteria, then using the rules it searches for a role of antibiotics that can be administered to the patient, based on past results. At the end the agent presents to physicians the top-five sets and the success percentage of each antibiotic. This paper presents the approach proposed and a test with a particular bacterium using real data provided by an Intensive Care Unit.
Silva, E.; Cardoso, L.; Portela, F.; Abelha, A.; Santos, M. F.; Machado, J.
Predicting nosocomial infection by using data mining technologies Proceedings Article
Em: A., Reis L. P. Rocha A. Rocha (Ed.): pp. 189-198, Springer Verlag, 2015, ISSN: 21945357, (cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Cost reduction; Data mining; Health care; Information systems; Patient treatment, Classification technique; Clinical decision support systems; CRISP-DM; Data mining technology; Healthcare environments; Healthcare institutions; Knowledge discovery in database; Nosocomial infection, Decision support systems
@inproceedings{Silva2015189,
title = {Predicting nosocomial infection by using data mining technologies},
author = {E. Silva and L. Cardoso and F. Portela and A. Abelha and M. F. Santos and J. Machado},
editor = {Reis L. P. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925325130&doi=10.1007%2f978-3-319-16528-8_18&partnerID=40&md5=043beaeb8726c7a044de6bc1cc09a0ef},
doi = {10.1007/978-3-319-16528-8_18},
issn = {21945357},
year = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {354},
pages = {189-198},
publisher = {Springer Verlag},
abstract = {The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naïve Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being. © Springer International Publishing Switzerland 2015.},
note = {cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559},
keywords = {Artificial intelligence; Cost reduction; Data mining; Health care; Information systems; Patient treatment, Classification technique; Clinical decision support systems; CRISP-DM; Data mining technology; Healthcare environments; Healthcare institutions; Knowledge discovery in database; Nosocomial infection, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naïve Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being. © Springer International Publishing Switzerland 2015.
2014
Pereira, E.; Brandão, A.; Salazar, M.; Portela, C. F.; Santos, M. F.; Machado, J.; Abelha, A.; Braga, J.
Pre-triage decision support improvement in maternity care by means of data mining Book Chapter
Em: pp. 175-192, IGI Global, 2014, ISBN: 9781466664784; 1466664770; 9781466664777, (cited By 3).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Data mining; Sensitivity analysis, Decision modeling; Decision support system (dss); Decision supports; Input variables; Management decisions; Manchester; Misclassifications; Waiting time, Decision support systems
@inbook{Pereira2014175,
title = {Pre-triage decision support improvement in maternity care by means of data mining},
author = {E. Pereira and A. Brandão and M. Salazar and C. F. Portela and M. F. Santos and J. Machado and A. Abelha and J. Braga},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946122274&doi=10.4018%2f978-1-4666-6477-7.ch009&partnerID=40&md5=aa8f639672408928c62c7d9bee639a7b},
doi = {10.4018/978-1-4666-6477-7.ch009},
isbn = {9781466664784; 1466664770; 9781466664777},
year = {2014},
date = {2014-01-01},
journal = {Integration of Data Mining in Business Intelligence Systems},
pages = {175-192},
publisher = {IGI Global},
abstract = {A triage system aims to make a correct characterization of the condition of patients. Because conventional triage systems like Manchester Triage System (MTS) are not suitable for maternity care, a decision model for pre-triaging patients in emergency (URG) and consultation (ARGO) classes was built and incorporated into a Decision Support System (DSS) implemented in Centro Materno Infantil do Norte (CMIN). Complementarily, DSS produces several indicators to support clinical and management decisions. A recent data analysis revealed a bias in the classification of URG cases. Frequently, cases classified as URG correspond to ARGO. This misclassification has been studied by means of Data Mining (DM) techniques in order to improve the pre-triage model and to discover knowledge for developing a new triage system based on waiting times and on a 5-scale of classes. This chapter presents a kind of sensitivity analysis combining input variables in six scenarios and considering four different DM techniques. CRISP-DM methodology was used to conduct the project. © 2015, IGI Global.},
note = {cited By 3},
keywords = {Artificial intelligence; Data mining; Sensitivity analysis, Decision modeling; Decision support system (dss); Decision supports; Input variables; Management decisions; Manchester; Misclassifications; Waiting time, Decision support systems},
pubstate = {published},
tppubtype = {inbook}
}
A triage system aims to make a correct characterization of the condition of patients. Because conventional triage systems like Manchester Triage System (MTS) are not suitable for maternity care, a decision model for pre-triaging patients in emergency (URG) and consultation (ARGO) classes was built and incorporated into a Decision Support System (DSS) implemented in Centro Materno Infantil do Norte (CMIN). Complementarily, DSS produces several indicators to support clinical and management decisions. A recent data analysis revealed a bias in the classification of URG cases. Frequently, cases classified as URG correspond to ARGO. This misclassification has been studied by means of Data Mining (DM) techniques in order to improve the pre-triage model and to discover knowledge for developing a new triage system based on waiting times and on a 5-scale of classes. This chapter presents a kind of sensitivity analysis combining input variables in six scenarios and considering four different DM techniques. CRISP-DM methodology was used to conduct the project. © 2015, IGI Global.
Brandão, A.; Pereira, E.; Portela, F.; Santos, M.; Abelha, A.; Machado, J.
Real-time Business Intelligence platform to maternity care Proceedings Article
Em: pp. 379-384, Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 9781479940844, (cited By 10; Conference of 3rd IEEE Conference on Biomedical Engineering and Sciences, IECBES 2014 ; Conference Date: 8 December 2014 Through 10 December 2014; Conference Code:111205).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Behavioral research; Biomedical engineering; Competitive intelligence; Data warehouses; Decision making; Information analysis; Interoperability; Medicine, Clinical outcome; Dimensional structures; Evidence-based practices; Incomplete information; Medical decision making; Medical errors; Patient health; Real time business intelligence, Decision support systems
@inproceedings{Brandão2014379,
title = {Real-time Business Intelligence platform to maternity care},
author = {A. Brandão and E. Pereira and F. Portela and M. Santos and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925625301&doi=10.1109%2fIECBES.2014.7047525&partnerID=40&md5=c9c0dbde3a55cfa9f6d17b3d89ebf795},
doi = {10.1109/IECBES.2014.7047525},
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 = {379-384},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The motivation for the implementation of a decision support system in maternity care came from the fact that people are constantly making quick decisions based on incomplete information. There is a significant impact on the patient health, as well as in increasing medical errors. To implement this system, it was resorted to the technologies of Business Intelligence, which involved the construction of two data warehouses with a dimensional structure in a star shape, for two distinct modules, in Gynecology and Obstetrics cares. The feasibility of an evidence-based practice and medical decision making in real time with universal and interoperable features are some of the benefits resulting from the implementation of decision support system in maternity care. In this paper we present the architecture of BI solution, some clinical outcomes and some benefits of the BI solution in a real world context. © 2014 IEEE.},
note = {cited By 10; 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 = {Artificial intelligence; Behavioral research; Biomedical engineering; Competitive intelligence; Data warehouses; Decision making; Information analysis; Interoperability; Medicine, Clinical outcome; Dimensional structures; Evidence-based practices; Incomplete information; Medical decision making; Medical errors; Patient health; Real time business intelligence, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The motivation for the implementation of a decision support system in maternity care came from the fact that people are constantly making quick decisions based on incomplete information. There is a significant impact on the patient health, as well as in increasing medical errors. To implement this system, it was resorted to the technologies of Business Intelligence, which involved the construction of two data warehouses with a dimensional structure in a star shape, for two distinct modules, in Gynecology and Obstetrics cares. The feasibility of an evidence-based practice and medical decision making in real time with universal and interoperable features are some of the benefits resulting from the implementation of decision support system in maternity care. In this paper we present the architecture of BI solution, some clinical outcomes and some benefits of the BI solution in a real world context. © 2014 IEEE.
2013
Portela, F.; Cabral, A.; Abelha, A.; Salazar, M.; Quintas, C.; Machado, J.; Neves, J.; Santos, M. F.
Knowledge acquisition process for intelligent decision support in critical health care Book Chapter
Em: pp. 55-68, IGI Global, 2013, ISBN: 9781466636682; 146663667X; 9781466636675, (cited By 23).
Resumo | Links | BibTeX | Etiquetas: Clinical conditions; Decision making process; Emergency departments; Intelligent decision support; Intelligent decision support systems; Manchester; New approaches; Patients' conditions, Decision making; Diagnosis; Hospitals; Knowledge acquisition, Decision support systems
@inbook{Portela201355,
title = {Knowledge acquisition process for intelligent decision support in critical health care},
author = {F. Portela and A. Cabral and A. Abelha and M. Salazar and C. Quintas and J. Machado and J. Neves and M. F. Santos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898101050&doi=10.4018%2f978-1-4666-3667-5.ch004&partnerID=40&md5=af2425c3e5775e5d88d36e3e76372ffb},
doi = {10.4018/978-1-4666-3667-5.ch004},
isbn = {9781466636682; 146663667X; 9781466636675},
year = {2013},
date = {2013-01-01},
journal = {Information Systems and Technologies for Enhancing Health and Social Care},
pages = {55-68},
publisher = {IGI Global},
abstract = {An efficient triage system is a good way to avoid some future problems and benefit the patient. However, a limitation still exists. The triage system is general and not specific to each case. Manchester Triage System is a reliable known system and is focused in the emergency department of a hospital. When applied to specific patients' conditions (such as pregnancy), it has several limitations. To overcome those limitations, an alternative triage system, integrated into an intelligent decision support system, was developed. The system classifies patients according to the severity of their clinical condition, establishing clinical priorities and not diagnosis. According to the urgency of attendance or problem type, it suggests one of three possible categories of the triage. This chapter presents the overall knowledge acquisition cycle associated with the workflow of patient arrival and the inherent decision making process. Results show that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place, reducing the waiting triage time and the number in general urgency. © 2013, IGI Global.},
note = {cited By 23},
keywords = {Clinical conditions; Decision making process; Emergency departments; Intelligent decision support; Intelligent decision support systems; Manchester; New approaches; Patients' conditions, Decision making; Diagnosis; Hospitals; Knowledge acquisition, Decision support systems},
pubstate = {published},
tppubtype = {inbook}
}
An efficient triage system is a good way to avoid some future problems and benefit the patient. However, a limitation still exists. The triage system is general and not specific to each case. Manchester Triage System is a reliable known system and is focused in the emergency department of a hospital. When applied to specific patients' conditions (such as pregnancy), it has several limitations. To overcome those limitations, an alternative triage system, integrated into an intelligent decision support system, was developed. The system classifies patients according to the severity of their clinical condition, establishing clinical priorities and not diagnosis. According to the urgency of attendance or problem type, it suggests one of three possible categories of the triage. This chapter presents the overall knowledge acquisition cycle associated with the workflow of patient arrival and the inherent decision making process. Results show that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place, reducing the waiting triage time and the number in general urgency. © 2013, IGI Global.
2009
Miranda, M.; Abelha, A.; Santos, M.; Machado, J.; Neves, J.
A group decision support system for staging of cancer Proceedings Article
Em: pp. 114-121, London, 2009, ISSN: 18678211, (cited By 16; Conference of 1st International Conference on Electronic Healthcare, eHealth 2008 ; Conference Date: 8 September 2008 Through 9 September 2008; Conference Code:85965).
Resumo | Links | BibTeX | Etiquetas: Cancer staging; Classification system; Common languages; Ehealth; Electronic medical record; Group decision; Health-care system; Healthcare facility; Specific information; Staging of caner; WEB application, Decision making; Diseases; E-learning; Health care; Intelligent agents; Medical computing; Multi agent systems; User interfaces, Decision support systems
@inproceedings{Miranda2009114,
title = {A group decision support system for staging of cancer},
author = {M. Miranda and A. Abelha and M. Santos and J. Machado and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870914962&doi=10.1007%2f978-3-642-00413-1_14&partnerID=40&md5=49bac329715400e5bec9a926be3ae06b},
doi = {10.1007/978-3-642-00413-1_14},
issn = {18678211},
year = {2009},
date = {2009-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering},
volume = {1 LNICST},
pages = {114-121},
address = {London},
abstract = {The TNM classification system was developed as a tool for physicians to stage different types of cancer based on standard criteria, according to a common language of cancer staging. Staging reports are usually performed by oncologists but sometimes are also done by physicians not specialized in this area. In this paper, it is presented a multi-agent system to support group decision that helps meeting participants to reach and to justify a solution. With the increasing use of web applications to perform the Electronic Medical Record on healthcare facilities, this system has the potential to be easily integrated in order to support the medical and clinical e-learning and to improve patient assistance. In fact, the usual need for documentation and specific information by the medical staff can be easily provided by these systems, making a new steep towards a paper free healthcare system. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009.},
note = {cited By 16; Conference of 1st International Conference on Electronic Healthcare, eHealth 2008 ; Conference Date: 8 September 2008 Through 9 September 2008; Conference Code:85965},
keywords = {Cancer staging; Classification system; Common languages; Ehealth; Electronic medical record; Group decision; Health-care system; Healthcare facility; Specific information; Staging of caner; WEB application, Decision making; Diseases; E-learning; Health care; Intelligent agents; Medical computing; Multi agent systems; User interfaces, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The TNM classification system was developed as a tool for physicians to stage different types of cancer based on standard criteria, according to a common language of cancer staging. Staging reports are usually performed by oncologists but sometimes are also done by physicians not specialized in this area. In this paper, it is presented a multi-agent system to support group decision that helps meeting participants to reach and to justify a solution. With the increasing use of web applications to perform the Electronic Medical Record on healthcare facilities, this system has the potential to be easily integrated in order to support the medical and clinical e-learning and to improve patient assistance. In fact, the usual need for documentation and specific information by the medical staff can be easily provided by these systems, making a new steep towards a paper free healthcare system. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2009.