Pervasive and intelligent decision support in Intensive Medicine – the complete picture

Series : Lecture notes in computer science (LNCS), vol. 8649In 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.(undefined

Real-time decision support in intensive medicine: an intelligent approach for monitoring data quality

Intensive Medicine is an area where big amounts of data are generated every day. The process to obtain knowledge from these data is extremely difficult and sometimes dangerous. The main obstacles of this process are the number of data collected manually and the quality of the data collected automatically. Information quality is a major constrain to the success of Intelligent Decision Support Systems (IDSS). This is the case of INTCare an IDSS which operates in real-time. Data quality needs to be ensured in a continuous way. The quality must be assured essentially in the data acquisition process and in the evaluation of the results obtained from data mining models. To automate this process a set of intelligent agents have been developed to perform a set of data quality tasks. This paper explores the data quality issues in IDSS and presents an intelligent approach for monitoring the data quality in INTCare system.Fundação para a Ciência e a Tecnologia (FCT

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 patient timeline for intensive care units

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.FCT -Fundação para a Ciência e a Tecnologia(PTDC/EEI-SII/1302/2012

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

Enabling ubiquitous data mining in intensive care: Features selection and data pre-processing

Ubiquitous Data Mining and Intelligent Decision Support Systems are gaining interest by both computer science researchers and intensive care doctors. Previous work contributed with Data Mining models to predict organ failure and outcome of patients in order to support and guide the clinical decision based on the notion of critical events and the data collected from monitors in real-time. This paper addresses the study of the impact of the Modified Early Warning Score, a simple physiological score that may allow improvements in the quality and safety of management provided to surgical ward patients, in the prediction sensibility. The feature selection and data pre-processing are also detailed. Results show that for some variables associated to this score the impact is minimal.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

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient 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 hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%

Pervasive intelligent decision support in critical health care

Tese de doutoramento (área de especialização em Tecnologias e Sistemas de Informação)Intensive Care Units (ICU) are recognized as being critical environments, due to the fact that patients admitted to these units typically find themselves in situations of organ failure or serious health conditions. ICU professionals (doctors and nurses) dedicate most of their time taking care for the patients, relegating to a second plan all documentation tasks. Tasks such as recording vital signs, treatment planning and calculation of indicators, are only performed when patients are in a stable clinical condition. These records can occur with a lag of several hours. Since this is a critical environment, the Process of Decision Making (PDM) has to be fast, objective and effective. Any error or delay in the implementation of a particular decision may result in the loss of a human life. Aiming to minimize the human effort in bureaucratic processes and improve the PDM, dematerialization of information is required, eliminating paper-based recording and promoting an automatic registration of electronic and real-time data of patients. These data can then be used as a complement to the PDM, e.g. in Decision Support Systems that use Data Mining (DM) models. At the same time it is important for PDM to overcome barriers of time and space, making the platforms as universal as possible, accessible anywhere and anytime, regardless of the devices used. In this sense, it has been observed a proliferation of pervasive systems in healthcare. These systems are focused on providing healthcare to anyone, anytime and anywhere by removing restrictions of time and place, increasing both the coverage and quality of health care. This approach is mainly based on information that is stored and available online. With the aim of supporting the PDM a set of tests were carried out using static DM models making use of data that had been collected and entered manually in Euricus database. Preliminary results of these tests showed that it was possible to predict organ failure and outcome of a patient using DM techniques considering a set of physiological and clinical variables as input. High rates of sensitivity were achieved: Cardiovascular - 93.4%; Respiratory - 96.2%; Renal - 98.1%; Liver - 98.3%; hematologic - 97.5%; and Outcome and 98.3%. Upon completion of this study a challenge emerged: how to achieve the same results but in a dynamic way and in real time? A research question has been postulated as: "To what extent, Intelligent Decision Support Systems (IDSS) may be appropriate for critical clinical settings in a pervasive way? “. Research work included: 1. To percept what challenges a universal approach brings to IDSS, in the context of critical environments; 2. To understand how pervasive approaches can be adapted to critical environments; 3. To develop and test predictive models for pervasive approaches in health care. The main results achieved in this work made possible: 1. To prove the adequacy of pervasive approach in critical environments; 2. To design a new architecture that includes the information requirements for a pervasive approach, able to automate the process of knowledge discovery in databases; 3. To develop models to support pervasive intelligent decision able to act automatically and in real time. To induce DM ensembles in real time able to adapt autonomously in order to achieve predefined quality thresholds (total error = 85 % and accuracy > = 60 %). Main contributions of this work include new knowledge to help overcoming the requirements of a pervasive approach in critical environments. Some barriers inherent to information systems, like the acquisition and processing of data in real time and the induction of adaptive ensembles in real time using DM, have been broken. The dissemination of results is done via devices located anywhere and anytime.As Unidades de Cuidados Intensivos (UCIs) são conhecidas por serem ambientes críticos, uma vez que os doentes admitidos nestas unidades encontram-se, tipicamente, em situações de falência orgânica ou em graves condições de saúde. Os profissionais das UCIs (médicos e enfermeiros) dedicam a maioria do seu tempo no cuidado aos doentes, relegando para segundo plano todas as tarefas relacionadas com documentação. Tarefas como o registo dos sinais vitais, o planeamento do tratamento e o cálculo de indicadores são apenas realizados quando os doentes se encontram numa situação clínica estável. Devido a esta situação, estes registos podem ocorrer com um atraso de várias horas. Dado que este é um ambiente crítico, o Processo de Tomada de Decisão (PTD) tem de ser rápido, objetivo e eficaz. Qualquer erro ou atraso na implementação de uma determinada decisão pode resultar na perda de uma vida humana. Com o intuito de minimizar os esforços humanos em processos burocráticos e de otimizar o PTD, é necessário proceder à desmaterialização da informação, eliminando o registo em papel, e promover o registo automático e eletrónico dos dados dos doentes obtidos em tempo real. Estes dados podem, assim, ser usados com um complemento ao PTD, ou seja, podem ser usados em Sistemas de Apoio à Decisão que utilizem modelos de Data Mining (DM). Ao mesmo tempo, é imperativo para o PTD superar barreiras ao nível de tempo e espaço, desenvolvendo plataformas tão universais quanto possíveis, acessíveis em qualquer lugar e a qualquer hora, independentemente dos dispositivos usados. Nesse sentido, tem-se verificado uma proliferação dos sistemas pervasive na saúde. Estes sistemas focam-se na prestação de cuidados de saúde a qualquer pessoa, a qualquer altura e em qualquer lugar através da eliminação das restrições ao nível do tempo e espaço, aumentando a cobertura e a qualidade na área da saúde. Esta abordagem é, principalmente, baseada em informações que estão armazenadas disponíveis online. Com o objetivo de suportar o PTD, foi realizado um conjunto de testes com modelos de DM estáticos, recorrendo a dados recolhidos e introduzidos manualmente na base de dados “Euricus”. Os resultados preliminares destes testes mostraram que era possível prever a falência orgânica ou a alta hospitalar de um doente, através de técnicas de DM utilizando como valores de entrada um conjunto de variáveis clínicas e fisiológicas. Nos testes efetuados, foram obtidos elevados níveis de sensibilidade: cardiovascular - 93.4%; respiratório - 96.2%; renal - 98.1%; hepático - 98.3%; hematológico - 97.5%; e alta hospitalar - 98.3%. Com a finalização deste estudo, observou-se o aparecimento de um novo desafio: como alcançar os mesmos resultados mas em modo dinâmico e em tempo real? Uma questão de investigação foi postulada: “Em que medida os Sistemas de Apoio à Decisão Inteligentes (SADIs) podem ser adequados às configurações clínicas críticas num modo pervasive?”. Face ao exposto, o trabalho de investigação inclui os seguintes pontos: 1. Perceber quais os desafios que uma abordagem universal traz para os SADIs, no contexto dos ambientes críticos; 2. Compreender como as abordagens pervasive podem ser adaptadas aos ambientes críticos; 3. Desenvolver e testar modelos de previsão para abordagens pervasive na área da saúde. Os principais resultados alcançados neste trabalho tornaram possível: 1. Provar a adequação da abordagem pervasive em ambientes críticos; 2. Conceber uma nova arquitetura que inclui os requisitos de informação para uma abordagem pervasive, capaz de automatizar o processo de descoberta de conhecimento em base de dados; 3. Desenvolver modelos de suporte à decisão inteligente e pervasive, capazes de atuar automaticamente e em tempo real. Induzir ensembles DM em tempo real, capazes de se adaptarem de forma autónoma, com o intuito de alcançar as medidas de qualidade pré-definidas (erro total = 85 % e acuidade> = 60 %). As principais contribuições deste trabalho incluem novos conhecimentos para ajudar a ultrapassar as exigências de uma abordagem pervasive em ambientes críticos. Algumas barreiras inerentes aos sistemas de informação, como a aquisição e o processamento de dados em tempo real e a indução de ensembles adaptativos em tempo real utilizando DM, foram transpostas. A divulgação dos resultados é feita através de dispositivos localizados, em qualquer lugar e a qualquer hora.Intensive Care Units (ICU) are recognized as being critical environments, due to the fact that patients admitted to these units typically find themselves in situations of organ failure or serious health conditions. ICU professionals (doctors and nurses) dedicate most of their time taking care for the patients, relegating to a second plan all documentation tasks. Tasks such as recording vital signs, treatment planning and calculation of indicators, are only performed when patients are in a stable clinical condition. These records can occur with a lag of several hours. Since this is a critical environment, the Process of Decision Making (PDM) has to be fast, objective and effective. Any error or delay in the implementation of a particular decision may result in the loss of a human life. Aiming to minimize the human effort in bureaucratic processes and improve the PDM, dematerialization of information is required, eliminating paper-based recording and promoting an automatic registration of electronic and real-time data of patients. These data can then be used as a complement to the PDM, e.g. in Decision Support Systems that use Data Mining (DM) models. At the same time it is important for PDM to overcome barriers of time and space, making the platforms as universal as possible, accessible anywhere and anytime, regardless of the devices used. In this sense, it has been observed a proliferation of pervasive systems in healthcare. These systems are focused on providing healthcare to anyone, anytime and anywhere by removing restrictions of time and place, increasing both the coverage and quality of health care. This approach is mainly based on information that is stored and available online. With the aim of supporting the PDM a set of tests were carried out using static DM models making use of data that had been collected and entered manually in Euricus database. Preliminary results of these tests showed that it was possible to predict organ failure and outcome of a patient using DM techniques considering a set of physiological and clinical variables as input. High rates of sensitivity were achieved: Cardiovascular - 93.4%; Respiratory - 96.2%; Renal - 98.1%; Liver - 98.3%; hematologic - 97.5%; and Outcome and 98.3%. Upon completion of this study a challenge emerged: how to achieve the same results but in a dynamic way and in real time? A research question has been postulated as: "To what extent, Intelligent Decision Support Systems (IDSS) may be appropriate for critical clinical settings in a pervasive way? “. Research work included: 1. To percept what challenges a universal approach brings to IDSS, in the context of critical environments; 2. To understand how pervasive approaches can be adapted to critical environments; 3. To develop and test predictive models for pervasive approaches in health care. The main results achieved in this work made possible: 1. To prove the adequacy of pervasive approach in critical environments; 2. To design a new architecture that includes the information requirements for a pervasive approach, able to automate the process of knowledge discovery in databases; 3. To develop models to support pervasive intelligent decision able to act automatically and in real time. To induce DM ensembles in real time able to adapt autonomously in order to achieve predefined quality thresholds (total error = 85 % and accuracy > = 60 %). Main contributions of this work include new knowledge to help overcoming the requirements of a pervasive approach in critical environments. Some barriers inherent to information systems, like the acquisition and processing of data in real time and the induction of adaptive ensembles in real time using DM, have been broken. The dissemination of results is done via devices located anywhere and anytime

A pervasive approach to a real-time intelligent decision support system in intensive medicine

The decision on the most appropriate procedure to provide to the patients the best healthcare possible is a critical and complex task in Intensive Care Units (ICU). Clinical Decision Support Systems (CDSS) should deal with huge amounts of data and online monitoring, analyzing numerous parameters and providing outputs in a short real-time. Although the advances attained in this area of knowledge new challenges should be taken into account in future CDSS developments, principally in ICUs environments. The next generation of CDSS will be pervasive and ubiquitous providing the doctors with the appropriate services and information in order to support decisions regardless the time or the local where they are. Consequently new requirements arise namely the privacy of data and the security in data access. This paper will present a pervasive perspective of the decision making process in the context of INTCare system, an intelligent decision support system for intensive medicine. Three scenarios are explored using data mining models continuously assessed and optimized. Some preliminary results are depicted and discussed.Fundação para a Ciência e a Tecnologia (FCT