An ecological model in support of an ontology of mental functioning
Maryanne J. Sacco, Guy Divita, Kathleen Coale, Howard H. Goldman, Carolyn P. Rosé

TL;DR
This paper introduces a new ontology to better extract mental functioning data from clinical records using natural language processing.
Contribution
The paper presents the Ecological Mental Functioning Ontology (EMFO) and its theoretical foundation for NLP-based mental functioning extraction.
Findings
An annotation schema based on the Ecological Model of Mental Functioning was shown to be robust for NLP methods.
The model captures the dynamic relationship between individuals, their activities, and environmental factors.
Manual annotation and consensus validated the schema on real and synthetic clinical notes.
Abstract
Health records contain rich sources of mental health data that can be used to evaluate disability and health care outcomes. However, a lack of behavioral health ontologies focused on daily life activity functioning has impeded development of clinical informatic tools to extract mental functioning information. We aim to present the theoretical foundation and conceptual model upon which the Ecological Mental Functioning Ontology (EMFO) was built to facilitate natural language processing (NLP) to extract mental functioning information in free-text clinical records. Subject matter experts operationally defined mental functioning, and a related theoretical perspective was established. Face validity of a proposed model was obtained using an iterative grounded theory approach. An annotation schema based on the model was constructed and tested using manual annotation and consensus on datasets…
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Taxonomy
TopicsMachine Learning in Healthcare · Mental Health via Writing · Digital Mental Health Interventions
