Predicting hospitalization following psychiatric crisis care using machine learning.

ABSTRACT:

Background

Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization.

Methods

Data from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable

SUBMITTER: Blankers M 

PROVIDER: S-EPMC7731561 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature