Payne, Philip R O
Washington University in St. Louis
10 papers on CoChatORCID 0000-0002-9532-2998
Most cited papers
- Verified
Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research
Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R. O., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., et al. (2013) · Medical Care
Electronic Health Records SystemsMedical Coding and Health Information
Cited by 567
- Verified
The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
Haendel, M. A., Chute, C. G., Bennett, T. D., Eichmann, D. A., Guinney, J., Kibbe, W. A., Payne, P. R. O., Pfaff, E. R., Robinson, P. N., Saltz, J. H., et al. (2021) · Journal of the American Medical Informatics Association
Research Data Management PracticesElectronic Health Records Systems
Cited by 553
- Verified
Questions for artificial intelligence in health care
Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. O. (2019) · JAMA
Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare
Cited by 385
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Electronic Health Record Data Quality Assessment and Tools: A Systematic Review
Lewis, A. E., Weiskopf, N., Abrams, Z. B., Foraker, R., Lai, A. M., Payne, P. R. O., & Gupta, A. (2023) · Journal of the American Medical Informatics Association
Electronic Health Records SystemsMedical Coding and Health Information
Cited by 171
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Clinical research informatics: challenges, opportunities and definition for an emerging domain
Embi, P. J., & Payne, P. R. O. (2009) · Journal of the American Medical Informatics Association
Electronic Health Records SystemsHealth and Medical Research Impacts
Cited by 163
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Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US
Lyons, P. G., Hofford, M. R., Yu, S. C., Michelson, A. P., Payne, P. R. O., Hough, C. L., & Singh, K. (2023) · JAMA Internal Medicine
Sepsis Diagnosis and TreatmentMachine Learning in Healthcare
Cited by 62
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Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy’s rule-based and machine learning-based methods
Bhattarai, K., Oh, I. Y., Sierra, J. M., Tang, J., Payne, P. R. O., Abrams, Z., & Lai, A. M. (2024) · JAMIA Open
Machine Learning in HealthcareBiomedical Text Mining and Ontologies
Cited by 45
- Verified
Comparison of Sepsis Definitions as Automated Criteria
Yu, S. C., Betthauser, K. D., Gupta, A., Lyons, P. G., Lai, A. M., Kollef, M. H., Payne, P. R. O., & Michelson, A. P. (2021) · Critical Care Medicine
Sepsis Diagnosis and TreatmentInflammation biomarkers and pathways
Cited by 29
- Verified
Advancing methodologies in Clinical Research Informatics (CRI): Foundational work for a maturing field
Embi, P. J., & Payne, P. R. O. (2014) · Journal of Biomedical Informatics
Electronic Health Records SystemsResearch Data Management Practices
Cited by 13
- Verified
M3NetFlow: A multi-scale multi-hop graph AI model for integrative multi-omic data analysis
Zhang, H., Goedegebuure, S. P., Ding, L., DeNardo, D., Fields, R. C., Province, M., Chen, Y., Payne, P., & Li, F. (2025) · iScience
Bioinformatics and Genomic NetworksComputational Drug Discovery Methods
Cited by 12
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