Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.
Joshua C SmithBrian D WilliamsonDavid J CronkiteDaniel ParkJill M WhitakerMichael F McLemoreJoshua T OsmanskiRobert WinterArvind RamaprasanAnn KelleyMary SheaSaranrat WittayanukornDanijela StojanovicYueqin ZhaoSengwee TohKevin B JohnsonDavid M AronoffDavid S CarrellPublished in: Journal of the American Medical Informatics Association : JAMIA (2024)
PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches.
Keyphrases
- machine learning
- coronavirus disease
- liver failure
- deep learning
- sars cov
- public health
- healthcare
- respiratory failure
- mental health
- aortic dissection
- drug induced
- health promotion
- respiratory syndrome coronavirus
- high throughput
- extracorporeal membrane oxygenation
- intensive care unit
- climate change
- mechanical ventilation
- acute respiratory distress syndrome