Wu, Yonghui

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

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

Association of Hemoglobin A 1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data Sciences and Informatics Initiative

Vashisht, R., Jung, K., Schuler, A., Banda, J. M., Park, R. W., Jin, S., Li, L., Dudley, J. T., Johnson, K. W., Shervey, M. M., et al. (2018) · JAMA Network Open

Diabetes Treatment and ManagementDiabetes Management and Research

Cited by 61

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

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

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

LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

Zen, H., Dang, V., Clark, R., Zhang, Y., Weiss, R. J., Jia, Y., Chen, Z., & Wu, Y. (2019) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 23

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

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

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

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