Modeling the vital sign space to detect the deterioration of patients in a pediatric intensive care unit

摘要

In the field of continuous vital-sign monitoring in critical care settings, it has been observed that the "early-warning signs" of impending physiological deterioration can fail to be detected timely and sometimes by resource-constrained clinical staff. This effect may be escalated by the "data deluge" caused by acquisition of more complex patient data during routine care. The objective of this study is to develop a probabilistic model for predicting the future clinical episodes of a patient using observed vital sign values prior to a clinical event. Vital signs (e.g. heart rate, blood pressure) are used to monitor a patient's physiological functions and their simultaneous changes indicate transitions between patient's health states. If such changes are abnormal then it may lead to serious physiological deterioration. The CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology was used as a data mining process and then we used Markov chains to identify the clinical states through which the patient passes. Then, a Hidden Markov model (HMM) based approach was applied for classification and prediction of patient's deterioration by computing the probability of future clinical states. Both learning models were trained and evaluated using six vital signs data from 94,678 records from 90 patient, collected from the database of real patients who were in the Pediatric Intensive Care Unit of the Central Military Hospital in the city of Bogota, Colombia. The proposed technique based on monitoring multiple physiological variables showed promising results in early identifying the deterioration of critically ill patients.