The Next Generation of Interoperability Agents in Healthcare

Cardoso, Luciana; Marins, Fernando Augusto Silva; Portela, Filipe; Santos, Manuel Filipe; Abelha, António; Machado, José

doi:10.3390/ijerph110505349

Cited by 67 publications

(34 citation statements)

References 28 publications

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“…Typ systems use intelligent agents (to perf automatically) [7] and artificial intellige predictions using Data Mining Techniques) goals. The decision support process is divid [8]: Intelligence, Design, Choice, Imp Monitoring.…”

Section: B Intelligent Decision Support Systemsmentioning

confidence: 99%

Preventing patient Cardiac Arrhythmias by using data mining techniques

Portela

Santos

Silva

et al. 2014

2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

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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.

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Section: B Intelligent Decision Support Systemsmentioning

confidence: 99%

Preventing patient Cardiac Arrhythmias by using data mining techniques

Portela

Santos

Silva

et al. 2014

2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

Self Cite

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“…Healthcare information systems are fundamental in managing, organizing and processing clinical information due to the fact that resources and time are critical in this environment [10][11][12].…”

Section: Archivos De Medicina Issn 1698-9465mentioning

confidence: 99%

“…There are many BI tools such as extract transform and load (ETL), data warehouse (DW), online analytical processing (OLAP), and dashboards [7][8][9]. Health Information Systems deals with resources, devices, and methods required to optimize the acquisition and use of information in health and biomedicine [10].…”

Section: Archivos De Medicina Issn 1698-9465mentioning

confidence: 99%

The Future of Records in Anesthesia: Development of an Anesthesia Data Warehouse

Machado¹

2017

J Healthc Commun

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The Healthcare system is one of the most complex activity sectors, essentially due to the different kind of concepts, processes, definitions, professionals and patients. Nowadays, information plays an increasingly role in the delivery of modern health care and efficiency of health systems. In this way, computer technology can be used to improve patient safety, the quality of care provided, and workload efficiency. In clinical anesthesia practice, appropriate application of informatics promotes data standardization and integrity, and supports clinical decision-making. In order to achieve these goals, many concepts like interoperability, usability and data access should be considered when Anesthesia Information Management Systems are concerned. This article describes current issues in anesthesia information management and suggests an Electronic Process Method of Extract-TransformLoad to upgrade these systems. A list of indicators was constructed recurring to PowerBI with the objective of a better information visualization.

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“…To support this architecture a set of intelligent agents [21,22] is used. First a ventilator is connected to a patient.…”

Section: Data Acquisition Architecturementioning

confidence: 99%

Critical Events in Mechanically Ventilated Patients

Portela

Santos

Machado

et al. 2016

New Advances in Information Systems and Technologies

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Abstract. Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a predefined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.

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The Next Generation of Interoperability Agents in Healthcare

Cited by 67 publications

References 28 publications

Preventing patient Cardiac Arrhythmias by using data mining techniques

Preventing patient Cardiac Arrhythmias by using data mining techniques

The Future of Records in Anesthesia: Development of an Anesthesia Data Warehouse

Critical Events in Mechanically Ventilated Patients

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