Dr. Ledys Maria Izquierdo Profile

Dr. Ledys Maria Izquierdo

at Univ Nacional de Colombia Sede Bogotá

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Proceedings Article | 3 November 2020 Paper

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

Ledys Izquierdo, Luis Fernando Niño, Jhon Sebastian Rojas

Proceedings Volume 11583, 115830Q (2020) https://doi.org/10.1117/12.2579629

KEYWORDS: Vital signs, Data modeling, Data processing, Data mining, Warning signs, Heart, Databases, Data acquisition, Blood pressure

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In the field of continuous vital-sign monitoring in critical care settings, it has been observed that the “earlywarning signs” of impending physiological deterioration can fail to be detected timely and sometimes by resourceconstrained 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 use Markov chains to identify the clinical states through which the patient passes. Then, a Hidden Markov model (HMM) based approach is 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 patient records, collected from the database of real patients who were in the Pediatric Intensive Care Unit of the Central Military Hospital in the city of Bogot´a, Colombia. The proposed technique based on monitoring multiple physiological variables showed promising results in early identifying the deterioration of critically ill patients.

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