Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.

ABSTRACT:

Background

Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.

Objective

The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers.

Methods

First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of t

SUBMITTER: Mar J 

PROVIDER: S-EPMC7592688 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature