A Machine-Assisted Framework for Ontology Development and Standardization: Case Study in Digital Health Technologies.

Fang Chen, Taylor B Harrison, Sunyang Fu, Ling He, Zhiyi Yue, Shuyu Lu, Liwei Wang, Xiaoyang Ruan, Hongfang Liu

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Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Fang ChenMcWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

Taylor B HarrisonMcWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

Sunyang FuMcWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

Ling HeKansas City University College of Osteopathic Medicine, Joplin, MO, USA.

Zhiyi YueSchool of Public Health, University of Texas Health Science Center, Houston, TX, USA.

Shuyu LuMcWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

Liwei WangMcWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

Xiaoyang RuanMcWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

Hongfang LiuMcWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital health technologies (DHTs) are reshaping healthcare by enabling personalized care, improving patient outcomes, and accelerating clinical research. However, the surge in DHT-related literature creates new challenges in effectively organizing, retrieving, and applying the resulting knowledge. Ontologies, structured frameworks for categorizing and connecting concepts, are central to meeting these challenges. Traditional ontology development in digital health often depends on manual processes, limiting efficiency, scalability, and cross-disciplinary adaptability. Building on previous work categorizing DHTs, we propose a new framework combining DHT lexicon extraction, ontology enrichment, and human-in-the-loop validation. In this study, we illustrate how the concept of a "adaptive ontology," powered by large language models, can classify and enhance DHT ontologies systematically, yet semi-automatically. Thus, providing a practical path to managing the evolving landscape of digital health.

Indexed as

Biological OntologiesDigital HealthNatural Language ProcessingVocabulary, ControlledHumansLarge Language Models

Identifiers

PMID41726495

PMCPMC12919423

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.