Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLP

TL;DR

This paper discusses developing an NLP-based tool to identify Adverse Childhood Experiences from clinical notes, addressing the lack of ACE ontologies and limited annotated data, to facilitate large-scale mental health research.

Contribution

It introduces a novel NLP approach utilizing ontologies to extract ACEs from clinical notes, overcoming existing challenges in data annotation and ontology availability.

Findings

Initial tool development for ACE detection from clinical notes

Potential to enable large-scale longitudinal mental health studies

Addresses gap in NLP resources for ACE identification

Abstract

Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence. They have been shown to be associated with increased risks of mental health diseases or other abnormal behaviours in later lives. However, the identification of ACEs from free-text Electronic Health Records (EHRs) with Natural Language Processing (NLP) is challenging because (a) there is no NLP ready ACE ontologies; (b) there are limited cases available for machine learning, necessitating the data annotation from clinical experts. We are currently developing a tool that would use NLP techniques to assist us in surfacing ACEs from clinical notes. This will enable us further research in identifying evidence of the relationship between ACEs and the subsequent developments of mental illness (e.g., addictions) in large-scale and longitudinal free-text EHRs, which has previously not been possible.