Internal Dataset
Self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients
UID: 10415
- Description
- We developed a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts.
- Local Expert
Access
- Restrictions
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Free to AllFree to all with registration
- Instructions
- As one of the NIH Generalist repositories, Zenodo enables researchers, scientists, EU projects and institutions to share the long tail of small research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science. Zenodo is an open access repository.
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MatLab
- Grant Support
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ME-1606-35555/Patient-Centered Outcomes Research Institute
- Other Resources
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NOCOS Calculator
Web calculator implementation of model
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