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 monitor
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tract. 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 %. Keywords: Data mining Intcare Intensive medicine Critical events Decision support Real-Time
Blood pressure
1 Introduction In critical environments the decision needs to be perform quickly and with a high level of accuracy. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. Intensive Medicine (IM) is a critical area of Medicine. Patients in weak conditions and with multiple diseases as is organ failure are cared every day. One of the most common complications is related to Blood Pressure with a constant values changing due to medical diseases, therapeutics or other procedures. Higher Blood pressure is associated to cardiovascular organ failure/diseases [1]. Nowadays the Intensive Care Units (ICU) are filled out with © Springer International Publishing Switzerland 2015 J. Bravo et al. (Eds.): AmIHEALTH 2015, LNCS 9456, pp. 77–90, 2015. DOI: 10.1007/978-3-319-26508-7_8
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many technical devices allowing a continuous patient monitoring. However these data are only used in the acquisition moment and they are not used to support the decision process. Having in consideration this aspect arises INTCare. INTCare [2] is a Pervasive Intelligent Decision Support System (PIDSS) able to collect and process data in real-time in order to provide new knowledge [3–7] anywhere and anytime. This knowledge is achieved by means of Data Mining (DM) techniques. This work is framed in INTCare project and it wants to develop DM models able to help the Intensivist to act in order to prevent Blood Pressure Critical Events (BPCE). Critical Events are defined as a continuous data acquisition of values out of the normal range for a determined period of time. BPCE can provoke hypotension and hypertension and leverage a set of other diseases as is heart attack, cardiovascular system failur
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