Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records.

ABSTRACT: The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 p

SUBMITTER: Mikolas P 

PROVIDER: S-EPMC9334289 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature