INTCARE: multi-agent approach for real-time intelligent decision support in intensive medicine

For an Intelligent Decision Support System to work in real-time, it is of great value the use of intelligent agents that cooperate with each other to accomplish their tasks. In a critical environment like an Intensive Care Unit, doctors should have the right information, at the right time, to better assist their patients. In this paper we present an architecture for a Multi-Agents System that will support doctors’ decision by in real-time, guaranteeing that all required clinical data is available and capable of predicting the patients’ condition for the next hour.Fundação para a Ciência e a Tecnologia (FCT

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The authors would like to thank FCT for the financial support through the contract PTDC/EEI - SII/1302/2012 (INTCare II

Towards of a real-time Big Data architecture to intensive care

These days the exponential increase in the volume and variety of data stored by companies and organizations of various sectors of activity, has required to organizations the search for new solutions to improve their services and/or products, taking advantage of technological evolution. As a response to the inability of organizations to process large quantities and varieties of data, in the technological market, arise the Big Data. This emerging concept defined mainly by the volume, velocity and variety has evolved greatly in part by its ability to generate value for organizations in decision making. Currently, the health care sector is one of the five sectors of activity where the potential of Big Data growth most stands out. However, the way to go is still long and in fact there are few organizations, related to health care, that are taking advantage of the true potential of Big Data. The main target of this research is to produce a real-time Big Data architecture to the INTCare system, of the Centro Hospitalar do Porto, using the main open source big data solution, the Apache Hadoop. As a result of the first phase of this research we obtained a generic architecture who can be adopted by other Intensive Care Units."This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013." This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-00002

Data mining predictive models for pervasive intelligent decision support in intensive care medicine

The introduction of an Intelligent Decision Support System (IDSS) in a critical area like the Intensive Medicine is a complex and difficult process. In this area, their professionals don’t have much time to document the cases, because the patient direct care is always first. With the objective to reduce significantly the manual records and, enabling, at the same time, the possibility of developing an IDSS which can help in the decision making process, all data acquisition process and knowledge discovery in database phases were automated. From the data acquisition to the knowledge discovering, the entire process is autonomous and executed in real-time. On-line induced data mining models were used to predict organ failure and outcome. Preliminary results obtained with a limited population of patients showed that this approach can be applied successfully.Fundação para a Ciência e a Tecnologia (FCT

Pervasive intelligent decision support system: technology acceptance in intensive care units

Intensive Care Units are considered a critical environment where the decision needs to be carefully taken. The real-time recognition of the condition of the patient is important to drive the decision process efficiently. In order to help the decision process, a Pervasive Intelligent Decision Support System (PIDSS) was developed. To provide a better comprehension of the acceptance of the PIDSS it is very important to assess how the users accept the system at level of usability and their importance in the Decision Making Process. This assessment was made using the four constructs proposed by the Technology Acceptance Methodology and a questionnaire-based approach guided by the Delphi Methodology. The results obtained so far show that although the users are satisfied with the offered information recognizing its importance, they demand for a faster system.Fundação para a Ciência e a Tecnologia (FCT

Knowledge discovery for pervasive and real-time intelligent decision support in intensive care medicine

Pervasiveness, real-time and online processing are important requirements included in the researchers’ agenda for the development of future generation of Intelligent Decision Support Systems (IDSS). In particular, knowledge discovery based IDSS operating in critical environments such of intensive care, should be adapted to those new requests. This paper introduces the way how INTCare, an IDSS developed in the intensive care unit of the Centro Hospitalar do Porto, will accommodate the new functionalities. Solutions are proposed for the most important constraints, e.g., paper based data, missing values, values out- of-range, data integration, data quality. The benefits and limitations of the approach are discussed.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/72819/ 2006, SFRH/BD/70156/201

Knowledge acquisition process for intelligent decision support in critical health care

An efficient triage system is a good way to avoid some future problems and, how much quicker it is, more the patient can benefit. However, a limitation still exists, the triage system are 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 the pregnancy 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 woman urgency of attendance or problem type, it suggests one of the three possible categories of the triage. This paper presents the overall knowledge acquisition cycle associated to the workflow of patient arrival and the inherent decision making process. Results showed 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 of women in general urgency.Fundação para a Ciência e a Tecnologia (FCT

Assessment of technology acceptance in intensive care units

The process of deploy a technology in critical services need to be very careful planned and processed. As an example it is the Intensive Care Unit (ICU). In the ICU the patients are in critically ill condit ions and there aren’t available time to make experiences or to develop incomplete systems. With the objective to improve the implementation process, the same should be accompanied in order to understand the environment and user behaviour. In this case and with the goal to evaluate the implementation process, an assessment model was applied to a real system called INTCare. INTCare is a Pervasive Intelligent Decision Support System (PIDSS). It was deployed in the ICU of Centro Hospitalar do Porto and was evaluated using the Technology Acceptance Model 3 (TAM). This assessment was made using the four constructs proposed by the TAM and a questionnaire-based approach guided by the Delphi Methodology. The results obtained so far show that although the users are satisfied with the offered information recognizing this importance, they demand for a faster system. This work present the main results achieved and suggest one way to follow when some technology is deployed in an environment like is ICU

A multi-agent platform for hospital interoperability

The interoperability among the Health Information Systems is a natural demand nowadays. The Agency for Integration, Diffusion and Archive of Medical Information (AIDA) is a Multi-Agent System (MAS) specifically developed to guarantee interoperability in health organizations. This paper presents the Biomedical Multi-agent Platform for Interoperability (BMaPI) integrated in AIDA and it is used by all hospital services which communicates with AIDA, one of the examples is the Intensive Care Unit. The BMaPI main objective is to facilitate the communication among the agents of a MAS. It also assists the interaction between humans and agents through an interface that allows the administrators to create new agents easily and to monitor their activities in real time. Due to the BMaPI characteristics it is possible ensure the continuous work of the AIDA agents associated to INTCare system. The BMaPI was installed in Centro Hospitalar do Porto successfully, increasing the functionality and overall usability of AIDA platform.(undefined

Plataforma de monitorização e suporte à decisão de doentes críticos

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 tomada de decisão (PTD), uma vez que, normalmente, não há tempo suficiente para analisar corretamente a situação clínica do doente. Para facilitar a aquisição de conhecimento e suportar o PTD por parte dos profissionais da UCI, foi desenvolvida uma plataforma global que, de entre as várias funcionalidades, permite um acompanhamento do doente e a previsão de eventos futuros de uma forma contínua e em tempo real, apresentando novos conhecimentos que podem contribuir de forma significativa para a melhoria da situação clínica de um doente.The complex situation of critical patients and the amount of data available in Intensive Care Units (ICU) makes difficult to obtain useful knowledge to the decision. Adding the fact that in ICU there is a large number of data on paper the decision maker cannot interpret correctly and in short time all the information acquired. In this context the human factor can cause errors in decision-making process (DMP), because normally the intensivist does not have enough time to properly analyse the clinical condition of the patient. To facilitate the acquisition of knowledge and support the ICU decision process by their professionals, a global platform was developed. Among the various features, this platform allows patient monitoring and forecasting future events continuously and in real time, presenting whenever is possible new knowledge which can contribute significantly to the improvement of the clinical status of a patient