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Skip search results from other journals and go to results- 7 Journal of Medical Internet Research
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Medication data was obtained from the National COVID Cohort Collaborative (N3 C), a national collection of 48 hospitals or data partners with 4.8 million patients [21]. The N3 C cohort is comprised of patients diagnosed with COVID-19 by polymerase chain reaction (PCR) and a control group of patients without COVID-19 matched by age, sex, and race at a 2:1 ratio.
JMIR Infodemiology 2024;4:e56675
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Recognizing the urgency of addressing s DHT usability-related challenges, a precompetitive collaboration within the Digital Health Measurement Collaborative Community (DATAcc) hosted by the Digital Medicine Society (Di Me) undertook a scoping review to highlight studies that have performed a usability-related evaluation for s DHTs, outline the dimensions of usability data that were assessed, and highlight the methods of usability evaluation.
J Med Internet Res 2024;26:e57628
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The resulting NLP algorithm has been published as part of the open-sourced Med Tagger NLP framework [9] and is being executed at 10 academic medical centers as part of their NLP data submissions to the National COVID-19 Cohort Collaborative (N3 C) data set [55].
JMIR Med Inform 2024;12:e49997
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Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study
Electronic health record (EHR) databases, such as the National COVID Cohort Collaborative (N3 C), provide an important tool for predicting, evaluating, and understanding PASC [3,4]. There is a broad range of PASC symptoms, diagnostic criteria, and hypothesized causal mechanisms, which has made it difficult for investigators to build generalizable predictions (Multimedia Appendix 1) [5-7].
JMIR Public Health Surveill 2024;10:e53322
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