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The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.

Laura SwinckelsFrank C BennisKirsten A ZiesemerJanneke Francisca Maria ScheermanHarmen BijwaardAnder de KeijzerJosef Jan Bruers
Published in: Journal of medical Internet research (2024)
Longitudinal EHRs proved to be helpful for support in health care. Current ML models on EHRs can support the detection of diseases in terms of accuracy and offer preliminary screening benefits. Regarding the prevention of diseases, ML and specifically DL models can accurately predict or detect diseases earlier than current clinical diagnoses. Adding personally responsible factors allows targeted prevention interventions. While ML models based on textual EHRs are still in the developmental stage, they have high potential to support clinicians and the health care system and improve patient outcomes.
Keyphrases
  • electronic health record
  • machine learning
  • deep learning
  • healthcare
  • cross sectional
  • artificial intelligence
  • physical activity
  • cancer therapy
  • risk assessment
  • convolutional neural network
  • clinical decision support