Multiple sclerosis risk stratification and healthcare cost prediction using machine learning.

ABSTRACT:

Background

The increasing availability of healthcare data offers an opportunity to address chronic diseases such as multiple sclerosis (MS) more proactively. We aimed to develop a machine learning (ML) approach to identify high-risk MS patients and predict their healthcare spending.

Methods

We conducted a retrospective analysis of de-identified commercial insurance claims from over 267,000 individuals (631 with MS), spanning January 2016 to June 2018. Monthly claims data were aggregated, and 72 ML regression and 63 classification models were trained to predict which patients would be in the top decile of healthcare spending over the subsequent four months. Model performance was compared with predictions based on four-month and one-month historical spending assessments.

Re

SUBMITTER: Wylezinski LS 

PROVIDER: S-EPMC12706058 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature