Hogan, William R.

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

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

Accuracy of Data in Computer-based Patient Records

Hogan, W. R., & Wagner, M. M. (1997) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 389

MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery.

Bihorac, A., Ozrazgat-Baslanti, T., Ebadi, A., Motaei, A., Madkour, M., Pardalos, P. M., Lipori, G., Hogan, W. R., Efron, P. A., Moore, F., et al. (2019) · Annals of Surgery

Cardiac, Anesthesia and Surgical OutcomesMachine Learning in Healthcare

Cited by 377

The All of Us Research Program: Data quality, utility, and diversity

Ramirez, A. H., Sulieman, L., Schlueter, D. J., Halvorson, A., Qian, J., Ratsimbazafy, F., Loperena, R., Mayo, K., Basford, M., Deflaux, N., et al. (2022) · Patterns

Electronic Health Records SystemsEthics in Clinical Research

Cited by 352

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

OneFlorida Clinical Research Consortium: linking a clinical and translational science institute with a community-based distributive medical education model

Shenkman, E., Hurt, M., Hogan, W., Carrasquillo, O., Smith, S., Brickman, A., & Nelson, D. (2018) · Academic Medicine

Health and Medical Research ImpactsHealth Policy Implementation Science

Cited by 97

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

Uncertainty-aware deep learning in healthcare: A scoping review

Loftus, T. J., Shickel, B., Ruppert, M. M., Balch, J. A., Ozrazgat-Baslanti, T., Tighe, P. J., Efron, P. A., Hogan, W. R., Rashidi, P., Upchurch, G. R., et al. (2022) · PLOS Digital Health

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 73

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

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 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

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

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

Developing a computable phenotype for identifying children, adolescents, and young adults with diabetes using electronic health records in the DiCAYA Network.

Shao, H., Thorpe, L. E., Islam, S., Bian, J., Guo, Y., Li, P., Bost, S., Dabelea, D., Conway, R., Crume, T., et al. (2025) · Diabetes Care

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 8