Refining Substance Use Classification: An Ontological Framework for Enhancing Large-Scale Data Collection

Area of Science:

• Biomedical Informatics
• Public Health
• Ontology Engineering

Background:

• Substance use disorders (SUD) present a significant public health challenge in the US.
• Current substance classification methods introduce ambiguities, impacting data accuracy and consistency in tracking SUD trends.
• The Office of Addiction Services and Support in New York State requires reliable data for federal reporting and public health initiatives.

Purpose of the Study:

• To establish the foundational structure of the Addiction Substance Ontology (ASO).
• To address challenges in substance classification for improved SUD data accuracy and consistency.
• To create a standardized taxonomy for tracking substance acquisition and use.

Main Methods:

• Applied Basic Formal Ontology (BFO) principles for ontology development.
• Defined substance classes using genus and differentiae based on chemical composition and usage characteristics.
• Developed an initial 143 classes based on a program admission use case.

Main Results:

• The foundational Addiction Substance Ontology (ASO) has been developed.
• The ASO provides a structured taxonomy for classifying substances relevant to SUD.
• An initial set of 143 substance classes has been created.

Conclusions:

• The ASO offers a robust framework to enhance the reliability of SUD data.
• Further pilot testing and stakeholder collaboration are essential for refining and validating the ASO.
• Successful implementation of the ASO can improve the tracking of SUD patterns and inform public health interventions.