Na Hong, PhD
Instructor of Biomedical Informatics and Data ScienceCards
Contact Info
Biomedical Informatics & Data Science
100 College Street
New Haven, CT 06510
United States
About
Titles
Instructor of Biomedical Informatics and Data Science
Biography
After obtaining my Ph.D. in Information Science and completing postdoctoral training in Medical Informatics, I have actively collaborated on interdisciplinary projects in the areas of Medicines and Informatics, which has enhanced my multidisciplinary background in medical informatics and digital health. My current research focuses on clinical information standards and standard-based data applications, involving data normalization, harmonization, ontology, and metadata development. I have also gained experience in medical literature mining, clinical predictive modeling using Electronic Health Records (EHRs), and clinical decision support systems. As a co-investigator or key researcher, I have contributed to several ongoing grants. I have also co-authored over 80 peer-reviewed journal articles, conference proceedings, and books.
Appointments
Biomedical Informatics & Data Science
InstructorPrimary
Other Departments & Organizations
Education & Training
- Research Fellow
- Mayo Clinic (2018)
- PhD
- Chinese Academy of Sciences (2010)
Research
Overview
My recent research focuses on medical data standards, such as OHDSI, FHIR, i2b2, etc, and standard-based data applications, including data normalization, harmonization, and large-scale research networks. I also have study experience in clinical predictive modeling using EHRs data and clinical decision support systems.
Public Health Interests
Research at a Glance
Publications Timeline
Publications
2022
Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning.
Lei H, Zhang M, Wu Z, Liu C, Li X, Zhou W, Long B, Ma J, Zhang H, Wang Y, Wang G, Gong M, Hong N, Liu H, Wu Y. Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning. Front Cardiovasc Med 2022, 9: 845210. PMID: 35321110, DOI: 10.3389/fcvm.2022.845210.Peer-Reviewed Original ResearchState of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.
Hong N, Liu C, Gao J, Han L, Chang F, Gong M, Su L. State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review. JMIR Med Inform 2022, 10: e28781. PMID: 35238790, DOI: 10.2196/28781.Peer-Reviewed Original Research
2021
Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models.
Su L, Xu Z, Chang F, Ma Y, Liu S, Jiang H, Wang H, Li D, Chen H, Zhou X, Hong N, Zhu W, Long Y. Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models. Front Med (Lausanne) 2021, 8: 664966. PMID: 34291058, DOI: 10.3389/fmed.2021.664966.Peer-Reviewed Original ResearchA Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study.
Li D, Gao J, Hong N, Wang H, Su L, Liu C, He J, Jiang H, Wang Q, Long Y, Zhu W. A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study. J Med Internet Res 2021, 23: e27118. PMID: 34014171, DOI: 10.2196/27118.Peer-Reviewed Original Research
2020
Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation.
Liu S, Wang Y, Wen A, Wang L, Hong N, Shen F, Bedrick S, Hersh W, Liu H. Implementation of a Cohort Retrieval System for Clinical Data Repositories Using the Observational Medical Outcomes Partnership Common Data Model: Proof-of-Concept System Validation. JMIR Med Inform 2020, 8: e17376. PMID: 33021486, DOI: 10.2196/17376.Peer-Reviewed Original Research
2019
Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries.
Hong N, Wen A, Stone DJ, Tsuji S, Kingsbury PR, Rasmussen LV, Pacheco JA, Adekkanattu P, Wang F, Luo Y, Pathak J, Liu H, Jiang G. Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries. J Biomed Inform 2019, 99: 103310. PMID: 31622801, DOI: 10.1016/j.jbi.2019.103310.Peer-Reviewed Original ResearchDeveloping a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data.
Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA Open 2019, 2: 570-579. PMID: 32025655, DOI: 10.1093/jamiaopen/ooz056.Peer-Reviewed Original ResearchUnderstanding the patient perspective of epilepsy treatment through text mining of online patient support groups.
He K, Hong N, Lapalme-Remis S, Lan Y, Huang M, Li C, Yao L. Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups. Epilepsy Behav 2019, 94: 65-71. PMID: 30893617, DOI: 10.1016/j.yebeh.2019.02.002.Peer-Reviewed Original ResearchADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model.
Yu Y, Ruddy KJ, Hong N, Tsuji S, Wen A, Shah ND, Jiang G. ADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model. J Biomed Inform 2019, 91: 103119. PMID: 30738946, DOI: 10.1016/j.jbi.2019.103119.Peer-Reviewed Original Research
2018
Automated Population of an i2b2 Clinical Data Warehouse using FHIR.
Solbrig HR, Hong N, Murphy SN, Jiang G. Automated Population of an i2b2 Clinical Data Warehouse using FHIR. AMIA Annu Symp Proc 2018, 2018: 979-988. PMID: 30815141.Peer-Reviewed Original Research
News
News
- October 21, 2024
Yale BIDS Presenting at the AMIA 2024 Annual Symposium
- September 27, 2024
Biomedical Informatics and Data Science (BIDS) Secures a $7.88 Million NIH Grant to Advance Mental Health Research Using AI Technology
- September 10, 2024
Using AI to Detect Autoimmune Diseases in Women
- June 17, 2024
Hot off the Press: Natural Language Processing in Biomedicine
Get In Touch
Contacts
Biomedical Informatics & Data Science
100 College Street
New Haven, CT 06510
United States
Locations
100 College Street
Academic Office
New Haven, CT 06510