Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.

ABSTRACT: Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in

SUBMITTER: Rajagopalan SS 

PROVIDER: S-EPMC11566279 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature