Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit.

ABSTRACT:

Background

Even brief hypotension is associated with increased morbidity and mortality. We developed a machine learning model to predict the initial hypotension event among intensive care unit (ICU) patients and designed an alert system for bedside implementation.

Materials and methods

From the Medical Information Mart for Intensive Care III (MIMIC-3) dataset, minute-by-minute vital signs were extracted. A hypotension event was defined as at least five measurements within a 10-min period of systolic blood pressure ≤ 90 mmHg and mean arterial pressure ≤ 60 mmHg. Using time series data from 30-min overlapping time windows, a random forest (RF) classifier was used to predict risk of hypotension every minute. Chronologically, the first half of extracted data was used to train th

SUBMITTER: Yoon JH 

PROVIDER: S-EPMC7687996 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature