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Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis.

Steven T SimonKaty E TrinkleyDaniel C MaloneMichael Aaron Rosenberg
Published in: Journal of medical Internet research (2022)
In summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.
Keyphrases
  • drug induced
  • liver injury
  • electronic health record
  • machine learning
  • deep learning
  • adverse drug
  • artificial intelligence
  • clinical decision support
  • high resolution
  • big data