Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.

Hong, Chuan; Liang, Liang; Yuan, Qianyu; Cho, Kelly; Liao, Katherine P; Pencina, Michael J; Christiani, David C; Cai, Tianxi

Hong, Chuan; Liang, Liang; Yuan, Qianyu; Cho, Kelly; Liao, Katherine P; Pencina, Michael J; Christiani, David C; Cai, Tianxi.

Affiliation

Hong C; Duke University, Durham, NC, USA; Harvard Medical School, Boston, MA, USA.
Liang L; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Yuan Q; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Cho K; Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA.
Liao KP; Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA.
Pencina MJ; Duke University, Durham, NC, USA.
Christiani DC; Harvard T.H. Chan School of Public Health, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA.
Cai T; Harvard T.H. Chan School of Public Health, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA. Electronic address: tcai@hsph.harvard.edu.

J Biomed Inform ; 144: 104425, 2023 08.

Article in En | MEDLINE | ID: mdl-37331495

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: COVID-19 / Lung Neoplasms Type of study: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Biomed Inform Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos

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