Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine

The role that decision making process plays in Intensive Medicine is very critical essential due to the bad health condition of the patients that go to Intensive Care Units (ICU) and the need of a quick and accurate decisions. Therefore each decision is crucial, because it can help saving endangered lives. The decision should be always taken in the patient best interest after analyzing all the data available. In the eyes of the intensivists, the ever growing amount of available data concerning the patients, makes it each time more difficult for them to make a decision based on so many information. It is based on this ideal of improving the decision making process, that this work arises and Data Mining models were induced to predict if a patient will need to take a vasopressor, more specifically: Dopamine, Adrenaline or Noradrenaline. This work used real data provided by an Intensive Care Unit and collected in real-time. The data mining model were induced using data from vital sign monitors, laboratory analysis and information about the patient’s Electronic Health Record. This study was based in clinical evidences and provided very useful results with a sensitivity around 90%. These models will reduce the need of vasopressor drugs by helping intensivists to act and take accurate decision before the vasopressor be need by the patient. It will improve the patient condition because when the time comes the predicted necessity of the vasopressor will cease to exist due to the early care provided by the intensivist. The decisions can be for example change the therapeutic plan. Overall, the decision making process becomes more reliable and effective and the quality of care given to patients is better.

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Real-Time Decision Support in Intensive Medicine: An intelligent approach for monitoring Data Quality

Portela¹,

Santos²,

Machado³

et al. 2013

JOMB

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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.  Index Terms-intensive care; data quality, real-time; intelligent decision support; intcare.

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Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine

Cited by 8 publications

References 19 publications

Pervasive Business Intelligence: A New Trend in Critical Healthcare

Pervasive Business Intelligence: A New Trend in Critical Healthcare

Data Mining to Predict the Use of Vasopressors in Intensive Medicine Patients

Real-Time Decision Support in Intensive Medicine: An intelligent approach for monitoring Data Quality

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