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Machine Learning to Develop and Internally Validate a Predictive Model for Post-operative Delirium in a Prospective, Observational Clinical Cohort Study of Older Surgical Patients.

Annie M RacineDouglas TommetMadeline L D'AquilaTamara G FongYun GouPatricia A TabloskiEran D MetzgerTammy T HshiehEva M SchmittSarinnapha M VasunilashornLisa KunzeKamen VlassakovAyesha AbdeenJeffrey LangeBrandon EarpBradford C DickersonEdward R MarcantonioJon SteingrimssonThomas G TravisonSharon K InouyeRichard N Jonesnull null
Published in: Journal of general internal medicine (2020)
We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.
Keyphrases
  • machine learning
  • cardiac surgery
  • hip fracture
  • artificial intelligence
  • big data
  • acute kidney injury
  • physical activity
  • deep learning
  • cross sectional
  • community dwelling