Back to Authors

Amith, Muhammad F

0000-0003-4333-1857

Mayo Clinic

School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.

Citations 5

Developing provider digital twins for personalized provider-patient communication via a RAG-based conversational framework

Computational and Structural Biotechnology Journal | December 19, 2025 | Volume: 32 | Issue: No issue available | Pages: 24-33

10.1016/j.csbj.2025.12.007 | PMID 41551040

Developing a High-Performing Network Computation of Big Bipartite Network Data Toward Alcohol Use Disorder Treatment Referrals

2025 19th International Conference on Semantic Computing (ICSC) | June 19, 2025 | Volume: No volume available | Issue: No issue available | Pages: 253-258

10.1109/icsc64641.2025.00044 | PMID 40600164

Developing a Computational Representation of Human Physical Activity and Exercise Using Open Ontology-Based Approach: A Tai Chi Use Case

2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) | August 22, 2024 | Volume: No volume available | Issue: No issue available | Pages: 31-39

10.1109/ichi61247.2024.00012 | PMID 39464170

Translating motivational interviewing for the HPV vaccine into a computable ontology model for automated AI conversational interaction

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems | May 11, 2024 | Volume: No volume available | Issue: No issue available | Pages: 1-12

10.1145/3613905.3651051 | PMID 38898884

Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO)

Journal of the American Medical Informatics Association | June 2, 2023 | Volume: 30 | Issue: 9 | Pages: 1465-1473

10.1093/jamia/ocad096 | PMID 37301740

Abstract Objective Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a “sick care” system into a “health-promoting” system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. Material and Methods Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed. Results We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results. Discussion SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations. Conclusion SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.