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Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.

, , , , , , , , , , , , , , , , , and . J. Am. Medical Informatics Assoc., 31 (3): 574-582 (February 2024)

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Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions., , , , , and . Appl. Clin. Inform., 09 (02): 313-325 (2018)Phenome-Wide Association Studies Using NLP-Derived Concepts., , , , , and . AMIA, AMIA, (2014)Data-driven automated classification algorithms for acute health conditions: Applying PheNorm to COVID-19 disease., , , , , , , , , and 9 other author(s). AMIA, AMIA, (2022)Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease., , , , , , , , , and 8 other author(s). J. Am. Medical Informatics Assoc., 31 (3): 574-582 (February 2024)Predictive Model for Inpatient Mortality., , , and . AMIA, AMIA, (2021)Reducing patient re-identification risk for laboratory results within research datasets., , , , and . JAMIA, 20 (1): 95-101 (2013)Natural language processing to identify lupus nephritis phenotype in electronic health records., , , , , , , , , and 7 other author(s). CoRR, (2021)Natural language processing to identify lupus nephritis phenotype in electronic health records., , , , , , , , , and 9 other author(s). BMC Medical Informatics Decis. Mak., 22-S (2): 348 (December 2022)Real-time Clinical Note Monitoring to Detect Conditions for Follow-up: a Case Study of Clinical Trial Enrollment in Drug-induced Torsades de Pointes and Stevens-Johnson Syndrome., , , , , , , , , and 3 other author(s). AMIA, AMIA, (2020)Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome., , , , , , , , , and 3 other author(s). J. Am. Medical Informatics Assoc., 28 (1): 126-131 (2021)