pubs2024

@inproceedings{Gonçalves2018352,

title = {Step towards a pervasive data system for intensive care medicine},

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

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045347385&doi=10.1007%2f978-3-319-77700-9_35&partnerID=40&md5=137db091342fb3d50c87c2f7ce531156},

doi = {10.1007/978-3-319-77700-9_35},

issn = {21945357},

year  = {2018},

date = {2018-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {747},

pages = {352-362},

publisher = {Springer Verlag},

abstract = {The use of technologies that can facilitate and streamline the processes of those who constantly need to perform various actions or comply with the most varied procedures, requires constant adaptability, either by organizations or users. The focus of this research is precisely the adaptability of technologies and in this case the technology used in the Intensive Care Unit (ICU) of the Centro Hospitalar do Porto (CHP). The increasing use of different electronic devices, all with different characteristics and dimensions, requires the optimization of the platforms that transmit and manipulate all the information, so that it is possible to use it regardless of which device is being used. Through the introduction of new functionalities to the current system, it is intended with this artefact to show the optimization made on the INTCare platform, with the main purpose of increasing its responsiveness. © Springer International Publishing AG, part of Springer Nature 2018.},

note = {cited By 0; Conference of 6th World Conference on Information Systems and Technologies, WorldCIST 2018 ; Conference Date: 27 March 2018 Through 29 March 2018; Conference Code:212469},

keywords = {Data systems; Electronic device; INTCare; Intensive care medicines; Pervasive environments; Pervasive healths, Information systems; Information use; mHealth; Ubiquitous computing, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Braga2016527,

title = {Pervasive patient timeline for intensive care units},

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

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961613566&doi=10.1007%2f978-3-319-31307-8_55&partnerID=40&md5=c888db54cdfaeb5dc704f1a54eed942f},

doi = {10.1007/978-3-319-31307-8_55},

issn = {21945357},

year  = {2016},

date = {2016-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {445},

pages = {527-536},

publisher = {Springer Verlag},

abstract = {This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts. © Springer International Publishing Switzerland 2016.},

note = {cited By 4; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},

keywords = {Clinical information; Critical care; Decision process; INTCare; Patient-centred; Pervasive Patient Timeline; Timeline; Vital sign, Data mining; Information systems, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Marins20141082,

title = {Extending a patient monitoring system with identification and localisation},

author = {F. Marins and R. Rodrigues and F. Portela and M. Santos and A. Abelha and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914113050&doi=10.1109%2fIEEM.2013.6962577&partnerID=40&md5=556dc04f5bb84afe83f367467d7bf947},

doi = {10.1109/IEEM.2013.6962577},

issn = {21573611},

year  = {2014},

date = {2014-01-01},

journal = {IEEE International Conference on Industrial Engineering and Engineering Management},

pages = {1082-1086},

publisher = {IEEE Computer Society},

abstract = {Intensive Care Units (ICUs) are a good environment for the application of intelligent systems in the healthcare area because it requires diagnosing, monitoring, and treatment of patients with critical illness. An intelligent decision support system, named INTCare, was developed and tested in CHP, a hospital center in Oporto. The need to detect the presence or absence of the patient in room, in order to stop the collection of redundant data concerning about the patient vital status led to the development of an RFID localisation and monitoring system - PaLMS, able to uniquely and unambiguously identify a patient and perceive its presence in room, making the process of data collection and alert event more accurate. The solution was the implementation of an intelligent multi-Agent system that connects the Patient Management System module, the INTCare module and the RFID equipment, using the HL7 standard embedded in agents behaviours. © 2013 IEEE.},

note = {cited By 2; Conference of 2013 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2013 ; Conference Date: 10 December 2013 Through 13 December 2013; Conference Code:109355},

keywords = {Ambient intelligence; Data acquisition; Decision support systems; Diagnosis; Intelligent agents; Intelligent systems; Multi agent systems; Patient monitoring; Patient treatment; Radio frequency identification (RFID), Data collection; Intelligent decision support systems; Intelligent multi agent systems; Medical informatics; Monitoring system; Patient management; Patient monitoring systems; Redundant data, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela2014287,

title = {A pervasive intelligent system for scoring MEWS and TISS-28 in intensive care},

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

editor = {Goh J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928230268&doi=10.1007%2f978-3-319-02913-9_73&partnerID=40&md5=512f655e3e01e666bd3666c00d4eec95},

doi = {10.1007/978-3-319-02913-9_73},

issn = {16800737},

year  = {2014},

date = {2014-01-01},

journal = {IFMBE Proceedings},

volume = {43},

pages = {287-290},

publisher = {Springer Verlag},

abstract = {Usually intensive care medicine practice is highly supported on scores as SOFA, SAPS and Glasgow. More recently, two new scores have been considered: MEWS and TISS-28.This paper presents how the Scoring System (SS) of the Intensive Care Unit (ICU) of Centro Hospitalar do Porto, evolved in order to accommodate the new scores. SS can automatically, in real-time and using online learning, provide a set of scores with a minimum human effort. SS is integrated in the Electronic Nursing Record (ENR) used for better comprehension of the patient condition through a set of information available: vital signs graphs, therapeutic plans, interventions, laboratory results and others. Those systems are supported by a pervasive platform for monitoring patient data anywhere and anytime. SS calculates the TISS-28 Score per day and nursing turn and the MEWS per minute, hour and day. These two new scores improve the understanding of the real condition of the patients. SS allows for obtaining automatically and in real-time the following scores: SAPS II, SAPS III, SOFA, GLASGOW, TISS28 and Mews anywhere and anytime. The development of SS only was possible due to a continuous and real-time execution of the data acquisition and data processing. The introduction of MEWS and TISS-28 brought some benefits at level of the decision process and workload and, consequently, to the ICU management process. © Springer International Publishing Switzerland 2014.},

note = {cited By 3; Conference of 15th International Conference on Biomedical Engineering, ICBME 2013 ; Conference Date: 4 December 2013 Through 7 December 2013; Conference Code:117089},

keywords = {Biomedical engineering; Data acquisition; Data handling; Hospital data processing; Intelligent systems; Medicine; Nursing; Patient monitoring; Patient treatment, INTCare; Intensive care medicines; Management process; MEWS; Patient condition; Real time execution; Scoring systems; TISS-28, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Veloso2014245,

title = {Real-time data mining models for predicting length of stay in Intensive Care Units},

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. Liu K.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84909994261&doi=10.5220%2f0005083302450254&partnerID=40&md5=0a83bbca2a0b3c9c92b40d6707334e16},

doi = {10.5220/0005083302450254},

isbn = {9789897580505},

year  = {2014},

date = {2014-01-01},

journal = {KMIS 2014 - Proceedings of the International Conference on Knowledge Management and Information Sharing},

pages = {245-254},

publisher = {INSTICC Press},

abstract = {Nowadays the efficiency of costs and resources planning in hospitals embody a critical role in the management of these units. Length Of Stay (LOS) is a good metric when the goal is to decrease costs and to optimize resources. In Intensive Care Units (ICU) optimization assumes even a greater importance derived from the high costs associated to inpatients. This study presents two data mining approaches to predict LOS in an ICU. The first approach considered the admission variables and some other physiologic variables collected during the first 24 hours of inpatient. The second approach considered admission data and supplementary clinical data of the patient (vital signs and laboratory results) collected in real-time. The results achieved in the first approach are very poor (accuracy of 73 %). However, when the prediction is made using the data collected in real-time, the results are very interesting (sensitivity of 96.104%). The models induced in second experiment are sensitive to the patient clinical situation and can predict LOS according to the monitored variables. Models for predicting LOS at admission are not suited to the ICU particularities. Alternatively, they should be induced in real-time, using online-learning and considering the most recent patient condition when the model is induced.},

note = {cited By 11; Conference of 6th International Conference on Knowledge Management and Information Sharing, KMIS 2014 ; Conference Date: 21 October 2014 Through 24 October 2014; Conference Code:114703},

keywords = {Clinical situations; INTCare; Length of stay; Online learning; Patient condition; Real time; Real-time data mining; Resources planning, Data mining; Forecasting; Knowledge management, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela201487,

title = {Pervasive and intelligent decision support in intensive medicine - The complete picture},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906886530&doi=10.1007%2f978-3-319-10265-8_9&partnerID=40&md5=0e641a2fd40b48001fee00eaeea8f97d},

doi = {10.1007/978-3-319-10265-8_9},

issn = {03029743},

year  = {2014},

date = {2014-01-01},

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

volume = {8649 LNCS},

pages = {87-102},

publisher = {Springer Verlag},

address = {Munich},

abstract = {In the Intensive Care Units (ICU) it is notorious the high number of data sources available. This situation brings more complexity to the way of how a professional makes a decision based on information provided by those data sources. Normally, the decisions are based on empirical knowledge and common sense. Often, they don't make use of the information provided by the ICU data sources, due to the difficulty in understanding them. To overcome these constraints an integrated and pervasive system called INTCare has been deployed. This paper is focused in presenting the system architecture and the knowledge obtained by each one of the decision modules: Patient Vital Signs, Critical Events, ICU Medical Scores and Ensemble Data Mining. This system is able to make hourly predictions in terms of organ failure and outcome. High values of sensitivity where reached, e.g. 97.95% for the cardiovascular system, 99.77% for the outcome. In addition, the system is prepared for tracking patients' critical events and for evaluating medical scores automatically and in real-time. © 2014 Springer International Publishing.},

note = {cited By 48; Conference of 5th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2014 ; Conference Date: 2 September 2014 Through 2 September 2014; Conference Code:107313},

keywords = {Cardiovascular system; Decision support systems; Information science, Critical events; Decision modules; Decision-based; Empirical knowledge; Intelligent decision support; Number of datum; Pervasive systems; System architectures, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Aguiar2013177,

title = {Pervasive information systems to intensive care medicine: Technology acceptance model},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887660872&partnerID=40&md5=73d693941b90cb1efc126c47dc83d4c9},

isbn = {9789898565594},

year  = {2013},

date = {2013-01-01},

journal = {ICEIS 2013 - Proceedings of the 15th International Conference on Enterprise Information Systems},

volume = {1},

pages = {177-184},

address = {Angers},

abstract = {The usability of information systems in critical environments like Intensive Care Units (ICU) is far than the expected and desirable. Typically, ICUs have a set of not integrated information silos and a high number of data recorded in paper. Whenever ICU professionals need to make a decision they have to deal with a high number of data sources containing useful information. Unfortunately, they can't use those sources due to the difficulty of evaluating them in a correct time. Pervasive Intelligent Decision Support Systems (PIDSS), operating automatically and in real-time, can be used to improve the decision making if they are suited to the requirements of the ICU. In this work a PIDSS have been assessed in terms of quality and user acceptance making use of Technology Acceptance Model (TAM). TAM proved to be very useful when combined with Delphi method features to involve the professionals and to make the system usable.},

note = {cited By 5; Conference of 15th International Conference on Enterprise Information Systems, ICEIS 2013 ; Conference Date: 4 July 2013 Through 7 July 2013; Conference Code:100809},

keywords = {Artificial intelligence; Decision making; Decision support systems; Information systems, Critical environment; Delphi; Integrated informations; Intelligent decision support systems; Intensive care; Intensive care medicines; Pervasive information systems; Technology acceptance model, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela2013270,

title = {Data mining for real-time Intelligent Decision Support System in intensive care medicine},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877954083&partnerID=40&md5=2fe4f3dbeb72a99c6e8cfa657dc52383},

isbn = {9789898565389},

year  = {2013},

date = {2013-01-01},

journal = {ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence},

volume = {2},

pages = {270-276},

address = {Barcelona},

abstract = {The introduction of Intelligent Decision Support Systems (IDSS) in critical areas like Intensive Medicine is a complex and difficult process. The professionals of Intensive Care Units (ICU) haven't much time to register data because the direct care to the patients is always mandatory. In order to help doctors in the decision making process, the INTCare system has been deployed in the ICU of Centro Hospitalar of Porto, Portugal. INTCare is an IDSS that makes use of data mining models to predict the outcome and the organ failure probability for the ICU patients. This paper introduces the work carried out in order to automate the processes of data acquisition and data mining. The main goal of this work is to reduce significantly the manual efforts of the staff in the ICU. All the processes are autonomous and are executed in real-time. In particular, Decision Trees, Support Vector Machines and Naïve Bayes were used with online data to continuously adapt the predictive models. The data engineering process and achieved results, in terms of the performance attained, will be presented.},

note = {cited By 6; Conference of 5th International Conference on Agents and Artificial Intelligence, ICAART 2013 ; Conference Date: 15 February 2013 Through 18 February 2013; Conference Code:97005},

keywords = {Artificial intelligence; Automobile drivers; Data mining; Decision making; Decision support systems; Decision trees, Data engineering; Data mining models; Decision making process; Intelligent decision support systems; Intensive care medicines; Predictive models; Real time; Real-time Intelligent Decision Support Systems, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rodrigues2012851,

title = {Monitoring intelligent system for the Intensive Care Unit using RFID and multi-agent systems},

author = {R. Rodrigues and P. Gonçalves and M. Miranda and F. Portela and M. Santos and J. Neves and A. Abelha and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903839575&doi=10.1109%2fIEEM.2012.6837860&partnerID=40&md5=be7c692d65ed1bb3e473117cb6ab9fc1},

doi = {10.1109/IEEM.2012.6837860},

issn = {21573611},

year  = {2012},

date = {2012-01-01},

journal = {IEEE International Conference on Industrial Engineering and Engineering Management},

pages = {851-855},

publisher = {IEEE Computer Society},

address = {Hong Kong},

abstract = {In an environment where patients' lives are at stake, Intensive Care Units (ICUs) become a good scenario for the implementation of Ambient Intelligence, helping medical professionals in their task of retrieving the well-being to patients. INTCare project is a system that aims the real-time monitoring of patients, and predicts their outcome in a short period of time. When patients' vital signs get out of range, an alert system warns medical staff about the patient condition. PaLMS, a Patient Localization and Monitoring System, is being developed and tested in Centro Hospitalar do Porto - CHP, a hospital in Portugal. It uses RFID technology with a multi-agent architecture for communications inside hospital, thus providing a way to improve INTCare by ending the storage and analysis of redundant data, collected when the patient isn't in the bed, plus stopping the warning events triggered by the vital signs out of range. © 2012 IEEE.},

note = {cited By 11; Conference of 2012 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2012 ; Conference Date: 10 December 2012 Through 13 December 2012; Conference Code:106121},

keywords = {Ambient intelligence; Medical professionals; Monitoring system; Multiagent architecture; Patient condition; Patient localization; Real time monitoring; RFID Technology, Digital storage; Industrial engineering; Intelligent systems; Internet of things; Multi agent systems, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Portela2012260,

title = {Intelligent decision support in intensive care - Towards technology acceptance},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898957706&partnerID=40&md5=e0837d4a8fea6e393845366adc7f0e47},

year  = {2012},

date = {2012-01-01},

journal = {ESM 2012 - 2012 European Simulation and Modelling Conference: Modelling and Simulation 2012},

pages = {260-266},

publisher = {EUROSIS},

address = {Essen},

abstract = {Decision support technology acceptance is a critical factor in the success of the adoption this type of systems by the users. INTCARE is an intelligent decision support system for intensive care medicine. The main purpose of this system is to help the doctors and nurses making decisions more proactively based on the prediction of the organ failure and the outcome of the patients. To assure the adoption of INTCARE by the doctors and by the nurses, several requirements had taken into account: process dematerialization (information is now in electronic format); interoperability among the systems (the AIDA platform was used to inter operate with other information systems); online data acquisition and real-time processing (a set of software agents has been developed to accomplish these tasks). A technology acceptance methodology has been followed in the Intensive Care Unit (ICU) of Centro Hospitalar do Porto in order to assure the most perfect alignment between the functional and technical characteristics of INTCARE and the user expectations. Results showed that the ICU staff is permeable to the system. In general more than 90 % of the answers are scored with 4 or 5 points which gives a good motivation to continue the work. © 2012 EUROSIS-ETI.},

note = {cited By 4; Conference of 26th European Simulation and Modelling Conference, ESM 2012 ; Conference Date: 22 October 2012 Through 24 October 2012; Conference Code:104382},

keywords = {Artificial intelligence; Decision support systems; Interoperability; Modal analysis; Nursing; Technology, INTCARE; Intelligent decision support systems; Intensive care medicines; Real-time; Technology acceptance, Intensive care units},

pubstate = {published},

tppubtype = {inproceedings}

}