Identifying Protective Drugs for Parkinson's Disease in Health-Care Databases Using Machine Learning.

ABSTRACT:

Background

Available treatments for Parkinson's disease (PD) are only partially or transiently effective. Identifying existing molecules that may present a therapeutic or preventive benefit for PD (drug repositioning) is thus of utmost interest.

Objective

We aimed at detecting potentially protective associations between marketed drugs and PD through a large-scale automated screening strategy.

Methods

We implemented a machine learning (ML) algorithm combining subsampling and lasso logistic regression in a case-control study nested in the French national health data system. Our study population comprised 40,760 incident PD patients identified by a validated algorithm during 2016 to 2018 and 176,395 controls of similar age, sex, and region of residence, all followed sinc

SUBMITTER: Courtois E 

PROVIDER: S-EPMC10087353 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature