College of Public Health

Hua Min, PhD

Machine Learning and Inference Laboratory | Center for Discovery Science and Health Informatics

PhD, Computer Science, New Jersey Institute of Technology

Key Interests
Health Informatics | Medical Ontologies and Standards | Data Integration | EHR Systems

Research Focus

My research interest is in the areas of ontology construction and its applications in bioinformatics. I have developed structural methodologies for auditing medical ontologies such as UMLS, NCI Thesaurus, and SNOMED CT. I have also explored ontology applications as a formal mechanism for representing and implementing behavioral models in computer systems. I am currently exploring the expansion of ontology applications, including ontology-based data integration, ontology- based machine learning methods, data extraction and analysis from various EHR systems, and other computer science technologies to clinical and health informatics.

Current Projects

■ Predicting quality of life in cancer survivors using ontology-based machine learning methods: this project aims to advance ML methods with the assistance of the medical knowledge from ontologies. It facilitates the creation of individualized models for cancer patients with the aim of improving outcomes for cancer survivals.

■ Developing methods to improve the quality of large-scale medical terminologies: this project aims to develop methods to improve the quality of large-scale medical terminologies including UMLS, NCI Thesaurus, and SNOMED CT. It is a collaborative work with the Semantic Web & Ontologies Lab from NJIT.

Select Publications

H. Min et al., Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology. Journal of Biomedical Semantics 8(1), 39 (2017).

H. Min et al., Relating complexity and error rates of ontology concepts. More complex NCIt concepts have more errors. Methods of Information in Medicine 56(3), 200-208 (2017).

H. Min et al., A comprehensive multi-morbidity index for predicting mortality in ICU patients. Journal of Palliative Medicine 20(1), 35-41 (2017).

C. Ochs et al., Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies. Journal of the American Medical Informatics Association 22(3), 507-18 (2015).


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