Machine-Learning for Prescription Patterns: Random Forest in the Prediction of Dose and Number of Antipsychotics Prescribed to People with Schizophrenia.

ABSTRACT:

Objective

We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms.

Methods

In a cross-sectional design, a sample of community mental health service users (SUs; n = 368) with a primary diagnosis of schizophrenia was randomly selected. Socio-demographic and clinical features, including the number, total dose, and route of administration of the antipsychotic treatment were recorded. Information about the number and the length of psychiatric hospitalization was retrieved. Ordinary Least Square (OLS) regression and ML algorithms (i.e., random forest [RF], supported vector machine, K-nearest neighborhood, and Naïve Bayes) were used to estimate the predictors of total antipsychotic dosage and prescription of antip

SUBMITTER: Marchi M 

PROVIDER: S-EPMC9329108 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature