An interpretable computational phenotyping pipeline for adult autism using routine electronic health records

Introduction

Electronic health records (EHRs) provide new opportunities for computational phenotyping in mental health, but routinely collected diagnostic data are often sparse, heterogeneous, and challenging to interpret. While numerous computational approaches have been applied to identify patient subgroups, clinically useful phenotyping requires workflows capable of transforming routine diagnostic information into interpretable patient profiles. The goal of this work is to develop and evaluate an end-to-end computational phenotyping pipeline for routine ICD-10 diagnostic data and demonstrate its feasibility in adults with autism spectrum disorder (ASD).