Xu, Hua

Immediate psychological distress in quarantined patients with COVID-19 and its association with peripheral inflammation: A mixed-method study

Guo, Q., Zheng, Y., Shi, J., Wang, J., Li, G., Li, C., Fromson, J. A., Xu, Y., Liu, X., Xu, H., et al. (2020) · Brain, Behavior, and Immunity

COVID-19 and Mental HealthLong-Term Effects of COVID-19

Cited by 406

Data from clinical notes: a perspective on the tension between structure and flexible documentation

Rosenbloom, S. T., Denny, J. C., Xu, H., Lorenzi, N., Stead, W. W., & Johnson, K. B. (2011) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsNursing Diagnosis and Documentation

Cited by 404

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

Enhancing clinical concept extraction with contextual embeddings

Si, Y., Wang, J., Xu, H., & Roberts, K. (2019) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 322

Improving large language models for clinical named entity recognition via prompt engineering

Hu, Y., Chen, Q., Du, J., Peng, X., Keloth, V. K., Zuo, X., Zhou, Y., Li, Z., Jiang, X., Lu, Z., et al. (2024) · Journal of the American Medical Informatics Association

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 314

Benchmarking large language models for biomedical natural language processing applications and recommendations

Chen, Q., Hu, Y., Peng, X., Xie, Q., Jin, Q., Gilson, A., Singer, M. B., Ai, X., Lai, P.-T., Wang, Z., et al. (2025) · Nature Communications

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 219

A study of deep learning approaches for medication and adverse drug event extraction from clinical text

Wei, Q., Ji, Z., Li, Z., Du, J., Wang, J., Xu, J., Xiang, Y., Tiryaki, F., Wu, S., Zhang, Y., et al. (2020) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 129

A hybrid system for temporal information extraction from clinical text

Tang, B., Wu, Y., Jiang, M., Chen, Y., Denny, J. C., & Xu, H. (2013) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 117

Development and evaluation of an ensemble resource linking medications to their indications

Wei, W.-Q., Cronin, R. M., Xu, H., Lasko, T. A., Bastarache, L., & Denny, J. C. (2013) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesElectronic Health Records Systems

Cited by 106

Advancing entity recognition in biomedicine via instruction tuning of large language models

Keloth, V. K., Hu, Y., Xie, Q., Peng, X., Wang, Y., Zheng, A., Selek, M., Raja, K., Wei, C. H., Jin, Q., et al. (2024) · Bioinformatics

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 104

Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm

Duan, R., Boland, M. R., Liu, Z., Liu, Y., Chang, H. H., Xu, H., Chu, H., Schmid, C. H., Forrest, C. B., Holmes, J. H., et al. (2020) · Journal of the American Medical Informatics Association

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 99

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

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

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

Applying active learning to assertion classification of concepts in clinical text

Chen, Y., Mani, S., & Xu, H. (2012) · Journal of Biomedical Informatics

Machine Learning and AlgorithmsBiomedical Text Mining and Ontologies

Cited by 57

Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition

Li, J., Zhou, Y., Jiang, X., Natarajan, K., Pakhomov, S. V., Liu, H., & Xu, H. (2021) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 53

Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues

Das, A., Selek, S., Warner, A. R., Zuo, X., Hu, Y., Kuttichi Keloth, V., Li, J., Zheng, W. J., & Xu, H. (2022) · Proceedings of the 21st Workshop on Biomedical Language Processing

Speech and dialogue systemsDigital Mental Health Interventions

Cited by 47

SCOR: A secure international informatics infrastructure to investigate COVID-19

Raisaro, J. L., Marino, F., Troncoso-Pastoriza, J., Beau-Lejdstrom, R., Bellazzi, R., Murphy, R., Bernstam, E. V., Wang, H., Bucalo, M., Chen, Y., et al. (2020) · Journal of the American Medical Informatics Association

Privacy-Preserving Technologies in DataCOVID-19 Digital Contact Tracing

Cited by 40

Natural language processing of clinical documentation to assess functional status in patients with heart failure

Adejumo, P., Thangaraj, P. M., Dhingra, L. S., Aminorroaya, A., Zhou, X., Brandt, C., Xu, H., Krumholz, H. M., & Khera, R. (2024) · JAMA Network Open

Nursing Diagnosis and DocumentationMachine Learning in Healthcare

Cited by 36

Social determinants of health extraction from clinical notes across institutions using large language models

Keloth, V. K., Selek, S., Chen, Q., Gilman, C., Fu, S., Dang, Y., Chen, X., Hu, X., Zhou, Y., He, H., et al. (2025) · npj Digital Medicine

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 24

Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach.

Hu, Y., Keloth, V. K., Raja, K., Chen, Y., & Xu, H. (2023) · Bioinformatics

Biomedical Text Mining and OntologiesMeta-analysis and systematic reviews

Cited by 21

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions

Fu, S., Jia, H., Vassilaki, M., Keloth, V. K., Dang, Y., Zhou, Y., Garg, M., Petersen, R. C., St Sauver, J., Moon, S., et al. (2024) · Journal of Biomedical Informatics

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 17

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Keloth, V. K., Banda, J. M., Gurley, M., Heider, P. M., Kennedy, G., Liu, H., Liu, F., Miller, T., Natarajan, K., V Patterson, O., et al. (2023) · Journal of Biomedical Informatics

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 16

Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study

Yang, R., Zeng, Q., You, K., Qiao, Y., Huang, L., Hsieh, C.-C., Rosand, B., Goldwasser, J., Dave, A., Keenan, T., et al. (2024) · Journal of Medical Internet Research

Topic ModelingNatural Language Processing Techniques

Cited by 16