Leveraging Natural Language Processing for Psychiatric Phenotyping from Spanish Electronic Health Records: Enabling the Investigation of Transdiagnostic Symptom Profiles at Scale.

ABSTRACT:

Introduction

Clinical notes in electronic health records offer valuable insight into the symptom profiles and trajectories of patients with severe mental illness (SMI). However, systematically extracting symptoms at scale remains a challenge, especially in languages other than English. We developed a light, accurate, and interpretable natural language processing (NLP) algorithm to extract psychiatric phenotypes from Spanish clinical notes.

Methods

We selected a set of 136 core psychiatric phenotypes and annotated 4,000 clinical note sections (e.g., Chief Complaint, Plan; called "documents") and 240 complete visit notes (called "entries") from two psychiatric hospitals in Colombia: Hospital Mental de Antioquia (HOMO) and Clínica San Juan de Dios Manizales (CSJDM). For phenoty

SUBMITTER: De La Hoz JF 

PROVIDER: S-EPMC12266705 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature