Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors

A clustering approach for predicting readmissions in intensive medicine

Decision making assumes a critical role in the Intensive Medicine. Data Mining is emerging in the clinical area to provide processes and technologies for transforming data into useful knowledge to support clinical decision makers. Appling clustering techniques to the data available on the patients admitted into Intensive Care Units and knowing which ones correspond to readmissions, it is possible to create meaningful clusters that will represent the base characteristics of readmitted patients. Thus, exploring common characteristics it is possible to prevent discharges that will result into readmissions and then improve the patient outcome and reduce costs. Moreover, readmitted patients present greater difficulty to be recovered. In this work it was followed the Stability and Workload Index for Transfer (SWIFT). A subset of variables from SWIFT was combined with the results from laboratory exams, namely the Lactic Acid and the Leucocytes values, in order to create clusters to identify, in the moment of discharge, patients that probably will be readmitted

Real-time data mining models for predicting length of stay in intensive care units

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.(undefined

Data mining for real-time intelligent decision support system in intensive care medicine

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.(undefined

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