Pervasive Intelligent Decision Support in

Critical Health Care


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 < = 40 %, sensitivity > = 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.