This paper is in the following e-collection/theme issue:

Theme Issue: Medical Informatics and COVID-19 (124) Clinical Information and Decision Making (1858) Natural Language Processing (886) Clinical Information and Decision Making (1858) Natural Language Processing (886)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46267, first published .
Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis

Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis

Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis

Authors of this article:

Feier Chang1 Author Orcid Image ;   Jay Krishnan2 Author Orcid Image ;   Jillian H Hurst3 Author Orcid Image ;   Michael E Yarrington2 Author Orcid Image ;   Deverick J Anderson2 Author Orcid Image ;   Emily C O'Brien4, 5 Author Orcid Image ;   Benjamin A Goldstein1, 3, 4, 5 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Smith R, D’Agostino A, Stoddard P, Kamal A, Kelsey K, Li L, Lin W, Enanoria W, Rudman S, Hoover C. Estimating the Number of Primary vs Incidental COVID-19 Hospitalizations in Santa Clara County. Open Forum Infectious Diseases 2025;12(3) View
  2. Wang Z, Hurst J, Hong C, Goldstein B. Unsupervised Coverage Sampling to Enhance Clinical Chart Review Coverage for Computable Phenotype Development: Simulation and Empirical Study (Preprint). JMIR Medical Informatics 2025 View