Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events.

ABSTRACT:

Objective

To propose an algorithm that utilizes only timestamps of longitudinal electronic health record data to classify clinical deterioration events.

Materials and methods

This retrospective study explores the efficacy of machine learning algorithms in classifying clinical deterioration events among patients in intensive care units using sequences of timestamps of vital sign measurements, flowsheets comments, order entries, and nursing notes. We design a data pipeline to partition events into discrete, regular time bins that we refer to as timesteps. Logistic regressions, random forest classifiers, and recurrent neural networks are trained on datasets of different length of timesteps, respectively, against a composite outcome of death, cardiac arrest, and Rapid Response T

SUBMITTER: Fu LH 

PROVIDER: S-EPMC8363809 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature