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Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.
Wei WQ, Teixeira PL, Mo H, Cronin RM, Warner JL, Denny JC. Wei WQ, et al. J Am Med Inform Assoc. 2016 Apr;23(e1):e20-7. doi: 10.1093/jamia/ocv130. Epub 2015 Sep 2. J Am Med Inform Assoc. 2016. PMID: 26338219 Free PMC article.
OBJECTIVE: To evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications. ...CONCLUSION: Mul …
OBJECTIVE: To evaluate the phenotyping performance of three major electronic health record (EHR) components: Int …
Inferring multimodal latent topics from electronic health records.
Li Y, Nair P, Lu XH, Wen Z, Wang Y, Dehaghi AAK, Miao Y, Liu W, Ordog T, Biernacka JM, Ryu E, Olson JE, Frye MA, Liu A, Guo L, Marelli A, Ahuja Y, Davila-Velderrain J, Kellis M. Li Y, et al. Nat Commun. 2020 May 21;11(1):2536. doi: 10.1038/s41467-020-16378-3. Nat Commun. 2020. PMID: 32439869 Free PMC article.
Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, action
Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad ad
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