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Bhandarkar, Avanti

https://orcid.org/0000-0002-9297-5029

Mayo Clinic

Mayo Clinic

Citations 1

BSO-AD: An Ontology for Representing and Harmonizing Behavioral Social Knowledge in ADRD
Li, Haifang , Yu, Yue, Bhandarkar, Avanti , Kumar, Rakesh, Clark, Isaac Heath, Hu, Yutong , Cao, Weiguo, Zhao, Na, LI, FANG, Tao, Cui, Tao, Cui , Li, Fang , Clark, Isaac H, Li, Fang , Tao, Cui , Cao, Weiguo

No container title available | April 1, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.64898/2026.03.30.26349756 Get It

ABSTRACT Behavioral and social factors (BSFs) substantially influence the risk, onset, and progression of Alzheimer’s disease and related dementias (ADRD), yet BSF-related knowledge remains scattered across heterogeneous sources, posing critical challenges for data harmonization and evidence synthesis. To address this gap, we present the B ehavioral S ocial Data and Knowledge O ntology for AD RD (BSO-AD), a FAIR-compliant semantic resource for representing and harmonizing BSFs and ADRD-related knowledge. BSO-AD was developed following established ontology design principles with reuse of existing ontologies and controlled terminologies, including the Social Determinants of Health Ontology, Drug Repurposing-Oriented Alzheimer’s Disease Ontology, AD-Onto, ICD-9-CM, and ICD-10-CM. Relationships between BSFs and ADRD were derived through literature mining. BSO-AD contains 1,690 classes, 152 object properties, 49 data properties, and 40 annotation properties. Ontology evaluation through Hootation-based domain expert review and a scalable LLM-assisted ontology assessment framework demonstrated high domain coverage and strong semantic coherence. BSO-AD provides a semantic foundation for data harmonization and knowledge integration in BSF-related ADRD research, while the LLM-assisted evaluation framework supports scalable and automated ontology assessment.