Project/scholarship details
- Project/scholarship ID
126314
- Reference
PTDC/EEI-SII/1302/2012
- FundRef URI
- Funder
FCT - Fundação para a Ciência e a Tecnologia, I.P.
- Funder's country
Portugal
- Funding program
5876-PPCDTI
- Funding amount
126,038.00 €
- Start date
2013-06-01
- End date
2015-10-31
Documents
Pervasive and intelligent decision support in Intensive Medicine – the complete...
Portela, Filipe; Santos, Manuel Filipe; Machado, José Manuel; Abelha, António; Silva, Álvaro; Rua, FernandoSeries : Lecture notes in computer science (LNCS), vol. 8649; 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 informati...
Plataforma de monitorização e suporte à decisão de doentes críticos
Portela, Filipe; Santos, Manuel Filipe; Machado, José Manuel; Abelha, António; Silva, A.; Rua, F.A situação complexa dos doentes críticos e a quantidade de dados disponíveis dificultam a obtenção de conhecimento profícuo para a decisão. Acrescendo o facto de nas Unidades de Cuidados Intensivos (UCI) ainda existir um elevado número de dados em papel, o decisor não consegue interpretar corretamente e em tempo útil toda a informação adquirida. Neste contexto, o fator humano pode provocar erros no processo de ...
Pervasive real-time intelligent system for tracking critical events with intens...
Portela, Filipe; Gago, Pedro; Santos, Manuel; Machado, José Manuel; Abelha, António; Silva, Álvaro; Fernando RuaNowadays it is fundamental in critical areas as is Intensive Medicine to have intelligent systems that are able to support the decision making process (DMP) giving important information in the right moment. Some of the biggest problems faced by such systems are related both to the number and the different types of data sources present in Intensive Care Units (ICU). Even though in such a setting the values for s...
Predict hourly patient discharge probability in intensive care units using data...
Portela, Filipe; Veloso, Rui; Santos, Manuel Filipe; Machado, José Manuel; Abelha, António; Silva, Álvaro; Rua, Fernando; Oliveira, Sérgio Manuel CostaThe length of stay (LOS) is an important metric to manage hospital units since a correct prevision of the LOS can contribute to reduce costs and optimize resources. This metric become more fundamental in intensive care units (ICU) where controlling patient condition and predict clinical events is very di cult. A set of experiences was made using data mining techniques in order to predict something more ambitiou...
Predicting plateau pressure in intensive medicine for ventilated patients
Oliveira, Sérgio; Portela, Filipe; Santos, Manuel; Machado, José Manuel; Abelha, António; Silva, Álvaro; Rua, FernandoBarotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists i...
A Real-Time intelligent system for tracking patient condition
Portela, Filipe; Oliveira, Sérgio Manuel Costa; Santos, Manuel; Machado, José Manuel; Abelha, AntónioHospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to...
Predict hourly patient discharge probability in intensive care units using Data...
Portela, Filipe; Veloso, Rui; Oliveira, Sérgio Manuel Costa; Santos, Manuel; Abelha, António; Machado, José Manuel; Silva, Álvaro; Rua, FernandoThe length of stay (LOS) is an important metric to manage hospital units since a correct prevision of the LOS can contribute to reduce costs and optimize resources. This metric become more fundamental in intensive care units (ICU) where controlling patient condition and predict clinical events is very difficult. A set of experiences was made using data mining techniques in order to predict something more ambiti...
Using domain knowledge to improve intelligent decision support in intensive med...
Machado, José Manuel; Abelha, António; Rua, Fernando; Silva, Álvaro; Santos, Manuel Filipe; Portela, Filipe; Veloso, RuiNowadays 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 su...
Real-Time decision support using data mining to predict blood pressure critical...
Portela, Filipe; Santos, Manuel; Machado, José Manuel; Abelha, António; Rua, Fernando; Silva, ÁlvaroPatient 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...
Clustering barotrauma patients in ICU–A data mining based approach using ventil...
Oliveira, Sérgio Manuel Costa; Portela, Filipe; Santos, Manuel Filipe; Machado, José; Abelha, António; Silva, Álvaro; Rua, FernandoPredicting 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-me...
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