Bian, Jiang

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias

Yu, Z., Peng, C., Yang, X., Dang, C., Adekkanattu, P., Gopal Patra, B., Peng, Y., Pathak, J., Wilson, D. L., Chang, C.-Y., et al. (2024) · Journal of Biomedical Informatics

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 37

Prevalence of Alzheimer’s and Related Dementia Diseases and Risk Factors Among Transgender Adults, Florida, 2012‒2020

Guo, Y., Li, Q., Yang, X., Jaffee, M. S., Wu, Y., Wang, F., & Bian, J. (2022) · American Journal of Public Health

Dementia and Cognitive Impairment ResearchLGBTQ Health, Identity, and Policy

Cited by 36

GatorTron: A Large Language Model for Clinical Natural Language Processing

Yang, X., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., et al. (2022) · medRxiv

Machine Learning in HealthcareTopic Modeling

Cited by 33

Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

Yang, X., Zhang, H., He, X., Bian, J., & Wu, Y. (2020) · JMIR Medical Informatics

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 29

Facilitation or Hindrance: Physicians’ Perception on Best Practice Alerts (BPA) Usage in an Electronic Health Record System

Chen, H., Butler, E., Guo, Y., George, T., Modave, F., Gurka, M., & Bian, J. (2019) · Health Communication

Electronic Health Records SystemsTechnology and Human Factors in Education and Health

Cited by 24

Regional trends and disparities in newer GLP1 receptor agonist initiation among real‐world adult patients eligible for obesity treatment

Radwan, R. M., Lee, Y. A., Kotecha, P., Wright, D. R., Hernandez, I., Ramon, R., Donahoo, W. T., Chen, Y., Allen, J. M., Bian, J., et al. (2025) · Diabetes Obesity and Metabolism

Pharmacology and Obesity TreatmentBariatric Surgery and Outcomes

Cited by 24

Federated learning with multi-cohort real-world data for predicting the progression from mild cognitive impairment to Alzheimer's disease

Pan, J., Fan, Z., Smith, G. E., Guo, Y., Bian, J., & Xu, J. (2025) · Alzheimer's & Dementia

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 19

Geographic variation and racial disparities in adoption of newer glucose-lowering drugs with cardiovascular benefits among US Medicare beneficiaries with type 2 diabetes

Chen, W.-H., Li, Y., Yang, L., Allen, J. M., Shao, H., Donahoo, W. T., Billelo, L., Hu, X., Shenkman, E. A., Bian, J., et al. (2024) · PLOS ONE

Diabetes Treatment and ManagementDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 17

A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes

Huang, Y., Guo, J., Donahoo, W. T., Lee, Y. A., Fan, Z., Lu, Y., Chen, W.-H., Tang, H., Bilello, L., Saguil, A. A., et al. (2024) · Nature Communications

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 14

Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs

Adekkanattu, P., Furmanchuk, A., Wu, Y., Pathak, A., Patra, B. G., Bost, S., Morrow, D., Wang, G. H.-M., Yang, Y., Forrest, N. J., et al. (2024) · npj Digital Medicine

Machine Learning in HealthcareMental Health via Writing

Cited by 12

Apple Vision Pro for Healthcare: "The Ultimate Display"? -- Entering the Wonderland of Precision Medicine

Egger, J., Gsaxner, C., Chen, X., Bian, J., Kleesiek, J., & Puladi, B. (2023) · arXiv

Augmented Reality ApplicationsVirtual Reality Applications and Impacts

Cited by 9

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning

Zhou, M., Tang, A. S., Zhang, H., Xu, Z., Ke, A. M. C., Su, C., Huang, Y., Mantyh, W. G., Jaffee, M. S., Rankin, K. P., et al. (2025) · Journal of Biomedical Informatics

Dementia and Cognitive Impairment ResearchMachine Learning in Healthcare

Cited by 9

Me LLaMA: Foundation Large Language Models for Medical Applications

Xie, Q., Chen, Q., Chen, A., Peng, C., Hu, Y., Lin, F., Peng, X., Huang, J., Zhang, J., Keloth, V., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 8

Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine

Xu, J., Guo, Y., Wang, F., Xu, H., Lucero, R., Bian, J., & Prosperi, M. (2022) · BMJ Open

Meta-analysis and systematic reviewsAdvanced Causal Inference Techniques

Cited by 6

Advancing social determinants of health research and practice: Data, tools, and implementation

Li, C., Guo, J., Bian, J., & Becich, M. J. (2025) · Journal of Clinical and Translational Science

Health disparities and outcomesPublic Health and Social Inequalities

Cited by 5

Identification of outcome-oriented progression subtypes from mild cognitive impairment to Alzheimer's disease using electronic health records

Xu, J., Yin, R., Huang, Y., Gao, H., Wu, Y., Guo, J., Smith, G. E., DeKosky, S. T., Wang, F., Guo, Y., et al. (2023) · medRxiv

Dementia and Cognitive Impairment ResearchMachine Learning in Healthcare

Cited by 5

Newer glucose‐lowering drugs and risk of suicidal ideation and behaviours: A network meta‐analysis of randomized outcome trials

Lu, Y., Tang, H., Kotecha, P., Cho, H., Donahoo, W. T., Bian, J., & Guo, J. (2024) · Diabetes, Obesity and Metabolism

Diabetes Treatment and ManagementSuicide and Self-Harm Studies

Cited by 3