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

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1330) JMIR Theme Issue: COVID-19 Special Issue (2088) Outbreak and Pandemic Preparedness and Management (1454) Surveillance Systems (359) Natural Language Processing (490) Public Health Informatics (39)

Published on in Vol 8 , No 12 (2022) :December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41529, first published .
Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System

Authors of this article:

Deborah E Malden 1, 2 Author Orcid Image ;   Sara Y Tartof 2, 3 Author Orcid Image ;   Bradley K Ackerson 4 Author Orcid Image ;   Vennis Hong 2 Author Orcid Image ;   Jacek Skarbinski 5, 6 Author Orcid Image ;   Vincent Yau 7 Author Orcid Image ;   Lei Qian 2 Author Orcid Image ;   Heidi Fischer 2 Author Orcid Image ;   Sally F Shaw 2 Author Orcid Image ;   Susan Caparosa 2 Author Orcid Image ;   Fagen Xie 2 Author Orcid Image
Article Authors Cited by (1) Tweetations (4) Metrics

Journals

  1. Lewnard J, McLaughlin J, Malden D, Hong V, Puzniak L, Ackerson B, Lewin B, Kim J, Shaw S, Takhar H, Jodar L, Tartof S. Effectiveness of nirmatrelvir–ritonavir in preventing hospital admissions and deaths in people with COVID-19: a cohort study in a large US health-care system. The Lancet Infectious Diseases 2023 View