Development of an ontology to characterize mental functioning.

Area of Science:

• Ontology development
• Medical classification systems
• Natural Language Processing (NLP)

Background:

• Mental functioning classification is underdeveloped.
• The International Classification of Functioning, Disability, and Health (ICF) provides a framework for conceptualizing functioning.
• Clinical records contain valuable functional information for NLP applications.

Purpose of the Study:

• To develop a conceptual and methodological approach for integrating functional information into an ontology.
• To categorize mental functioning for improved classification.
• To support the United States (U.S.) Social Security Administration (SSA) in their use case.

Main Methods:

• Conceptualizing and defining mental functioning was essential for developing NLP tools.
• The ICF was used as a framework to conceptualize mental functioning at the activities and participation level.
• A mental functioning domain ontology was developed by rearranging, reclassifying, and incorporating ICF components.

Main Results:

• Direct alignment of ICF components into an applied ontology presented conceptual challenges.
• Rearrangement of ICF components was necessary to adequately support the social security disability determination process.
• The developed ontology effectively categorizes mental functioning.

Conclusions:

• The developed ontology provides a robust framework for classifying mental functioning.
• Findings have implications for future NLP efforts in behavioral health outcomes and policy research.
• The approach supports disability determination processes.