Zhou, Li

The Relationship between Electronic Health Record Use and Quality of Care over Time

Zhou, L., Soran, C. S., Jenter, C. A., Volk, L. A., Orav, E. J., Bates, D. W., & Simon, S. R. (2009) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 194

Hospital Readmission and Social Risk Factors Identified from Physician Notes

Navathe, A. S., Zhong, F., Lei, V. J., Chang, F. Y., Sordo, M., Topaz, M., Navathe, S. B., Rocha, R. A., & Zhou, L. (2018) · Health Services Research

Machine Learning in HealthcareMedical Coding and Health Information

Cited by 161

Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions

Wang, L., Sha, L., Lakin, J. R., Bynum, J., Bates, D. W., Hong, P., & Zhou, L. (2019) · JAMA Network Open

Machine Learning in HealthcarePalliative Care and End-of-Life Issues

Cited by 105

Speech recognition for clinical documentation from 1990 to 2018: a systematic review

Blackley, S. V., Huynh, J., Wang, L., Korach, Z., & Zhou, L. (2019) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsNursing Diagnosis and Documentation

Cited by 97

Analysis of errors in dictated clinical documents assisted by speech recognition software and professional transcriptionists

Zhou, L., Blackley, S. V., Kowalski, L., Doan, R., Acker, W. W., Landman, A. B., Kontrient, E., Mack, D., Meteer, M., Bates, D. W., et al. (2018) · JAMA Network Open

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 94

Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes

Du, X., Novoa-Laurentiev, J., Plasek, J. M., Chuang, Y.-W., Wang, L., Marshall, G. A., Mueller, S. K., Chang, F., Datta, S., Paek, H., et al. (2024) · eBioMedicine

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 69

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial

Rossetti, S. C., Dykes, P. C., Knaplund, C., Cho, S., Withall, J., Lowenthal, G., Albers, D., Lee, R. Y., Jia, H., Bakken, S., et al. (2025) · Nature Medicine

Healthcare Technology and Patient MonitoringMachine Learning in Healthcare

Cited by 54

Assessment of the accuracy of firearm injury intent coding at 3 US hospitals

Miller, M., Azrael, D., Yenduri, R., Barber, C., Bowen, A., MacPhaul, E., Mooney, S. J., Zhou, L., Goralnick, E., & Rowhani-Rahbar, A. (2022) · JAMA Network Open

Gun Ownership and Violence ResearchMedical Coding and Health Information

Cited by 52

Development and Validation of a Deep Learning Model for Detection of Allergic Reactions Using Safety Event Reports Across Hospitals

Yang, J., Wang, L., Phadke, N. A., Wickner, P. G., Mancini, C. M., Blumenthal, K. G., & Zhou, L. (2020) · JAMA Network Open

Pharmacovigilance and Adverse Drug ReactionsMachine Learning in Healthcare

Cited by 42

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

Rossetti, S. C., Knaplund, C., Albers, D., Dykes, P. C., Kang, M. J., Korach, T. Z., Zhou, L., Schnock, K., Garcia, J., Schwartz, J., et al. (2021) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 42

Mining clinical phrases from nursing notes to discover risk factors of patient deterioration

Korach, Z. T., Yang, J., Rossetti, S. C., Cato, K. D., Kang, M.-J., Knaplund, C., Schnock, K. O., Garcia, J. P., Jia, H., Schwartz, J. M., et al. (2020) · International Journal of Medical Informatics

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 40

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event

Korach, Z. T., Cato, K. D., Collins, S. A., Kang, M. J., Knaplund, C., Dykes, P. C., Wang, L., Schnock, K. O., Garcia, J. P., Jia, H., et al. (2019) · Applied Clinical Informatics

Machine Learning in HealthcareNursing Diagnosis and Documentation

Cited by 32

Risk of Thyroid Cancer Associated with Use of Liraglutide and Other Antidiabetic Drugs in a US Commercially Insured Population

Funch, D., Mortimer, K., Ziyadeh, N. J., Seeger, J. D., Zhou, L., Ng, E., Ross, D., Major-Pedersen, A., Bosch-Traberg, H., Gydesen, H., et al. (2021) · Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy

Metabolism, Diabetes, and CancerThyroid Cancer Diagnosis and Treatment

Cited by 24

Identifying nurses’ concern concepts about patient deterioration using a standard nursing terminology

Kang, M.-J., Dykes, P. C., Korach, T. Z., Zhou, L., Schnock, K. O., Thate, J., Whalen, K., Jia, H., Schwartz, J., Garcia, J. P., et al. (2020) · International Journal of Medical Informatics

Nursing Diagnosis and DocumentationMachine Learning in Healthcare

Cited by 24

Classifying firearm injury intent in electronic hospital records using natural language processing

MacPhaul, E., Zhou, L., Mooney, S. J., Azrael, D., Bowen, A., Rowhani-Rahbar, A., Yenduri, R., Barber, C., Goralnick, E., & Miller, M. (2023) · JAMA Network Open

Gun Ownership and Violence ResearchMachine Learning in Healthcare

Cited by 17