Bian, Jiang

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 1,542

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2019) · arXiv

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 1,538

A large language model for electronic health records

Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine

Machine Learning in HealthcareTopic Modeling

Cited by 847

Causal inference and counterfactual prediction in machine learning for actionable healthcare

Prosperi, M., Guo, Y., Sperrin, M., Koopman, J. S., Min, J. S., He, X., Rich, S., Wang, M., Buchan, I. E., & Bian, J. (2020) · Nature Machine Intelligence

Advanced Causal Inference TechniquesMachine Learning in Healthcare

Cited by 459

A study of generative large language model for medical research and healthcare

Peng, C., Yang, X., Chen, A., Smith, K. E., PourNejatian, N., Costa, A. B., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., et al. (2023) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 408

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Patra, B. G., Sharma, M. M., Vekaria, V., Adekkanattu, P., Patterson, O. V., Glicksberg, B., Lepow, L. A., Ryu, E., Biernacka, J. M., Furmanchuk, A., et al. (2021) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 248

Big data hurdles in precision medicine and precision public health

Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018) · BMC Medical Informatics and Decision Making

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 200

Algorithmic fairness in computational medicine

Xu, J., Xiao, Y., Wang, W. H., Ning, Y., Shenkman, E. A., Bian, J., & Wang, F. (2022) · eBioMedicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 170

Twitter Analysis: Studying US Weekly Trends in Work Stress and Emotion

Wang, W., Hernandez, I., Newman, D. A., He, J., & Bian, J. (2016) · Applied Psychology

Mental Health via WritingEmotion and Mood Recognition

Cited by 120

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records

Li, Q., Yang, X., Xu, J., Guo, Y., He, X., Hu, H., Lyu, T., Marra, D., Miller, A., Smith, G., et al. (2023) · Alzheimer's & Dementia

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 113

Newer glucose‐lowering drugs and risk of dementia: A systematic review and meta‐analysis of observational studies

Tang, H., Shao, H., Shaaban, C. E., Yang, K., Brown, J., Anton, S., Wu, Y., Bress, A., Donahoo, W. T., DeKosky, S. T., et al. (2023) · Journal of the American Geriatrics Society

Diabetes Treatment and ManagementDementia and Cognitive Impairment Research

Cited by 106

International Classification of Diseases, Tenth Revision, Clinical Modification social determinants of health codes are poorly used in electronic health records

Guo, Y., Chen, Z., Xu, K., George, T. J., Wu, Y., Hogan, W., Shenkman, E. A., & Bian, J. (2020) · Medicine

Medical Coding and Health InformationElectronic Health Records Systems

Cited by 95

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data

Bian, J., Lyu, T., Loiacono, A., Viramontes, T. M., Lipori, G., Guo, Y., Wu, Y., Prosperi, M., George, T. J., Harle, C. A., et al. (2020) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsData Quality and Management

Cited by 92

Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting

Yang, X., Bian, J., Fang, R., Bjarnadottir, R. I., Hogan, W. R., & Wu, Y. (2020) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 74

Medical foundation large language models for comprehensive text analysis and beyond

Xie, Q., Chen, Q., Chen, A., Peng, C., Hu, Y., Lin, F., Peng, X., Huang, J., Zhang, J., Keloth, V., et al. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 74

A study of deep learning methods for de-identification of clinical notes in cross-institute settings

Yang, X., Lyu, T., Li, Q., Lee, C.-Y., Bian, J., Hogan, W. R., & Wu, Y. (2019) · BMC Medical Informatics and Decision Making

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 73

A single-cell and spatial RNA-seq database for Alzheimer’s disease (ssREAD)

Wang, C., Acosta, D., McNutt, M., Bian, J., Ma, A., Fu, H., & Ma, Q. (2024) · Nature Communications

Single-cell and spatial transcriptomicsAlzheimer's disease research and treatments

Cited by 73

High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data

Zang, C., Zhang, H., Xu, J., Zhang, H., Fouladvand, S., Havaldar, S., Cheng, F., Chen, K., Chen, Y., Glicksberg, B. S., et al. (2023) · Nature Communications

Advanced Causal Inference TechniquesAlzheimer's disease research and treatments

Cited by 69

MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes

Yang, X., Bian, J., Gong, Y., Hogan, W. R., & Wu, Y. (2019) · Drug Safety

Biomedical Text Mining and OntologiesPharmacovigilance and Adverse Drug Reactions

Cited by 66

A scoping review of fair machine learning techniques when using real-world data

Huang, Y., Guo, J., Chen, W.-H., Lin, H.-Y., Tang, H., Wang, F., Xu, H., & Bian, J. (2024) · Journal of Biomedical Informatics

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 61

Generative Artificial Intelligence for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations: An ISPOR Working Group Report

Fleurence, R. L., Bian, J., Wang, X., Xu, H., Dawoud, D., Higashi, M., & Chhatwal, J. (2025) · Value in Health

Artificial Intelligence in Healthcare and EducationHealth Systems, Economic Evaluations, Quality of Life

Cited by 54

Glucagon-Like Peptide-1 Receptor Agonists and Risk for Suicidal Ideation and Behaviors in U.S. Older Adults With Type 2 Diabetes: A Target Trial Emulation Study

Tang, H., Lu, Y., Donahoo, W. T., Shao, H., Shi, L., Fonseca, V. A., Guo, Y., Bian, J., & Guo, J. (2024) · Annals of Internal Medicine

Diabetes Treatment and ManagementSuicide and Self-Harm Studies

Cited by 40

Contextualized medication information extraction using Transformer-based deep learning architectures

Chen, A., Yu, Z., Yang, X., Guo, Y., Bian, J., & Wu, Y. (2023) · Journal of Biomedical Informatics

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 40

A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models

Yu, Z., Yang, X., Dang, C., Wu, S., Adekkanattu, P., Pathak, J., George, T. J., Hogan, W. R., Guo, Y., Bian, J., et al. (2021) · arXiv

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 38