Google Research

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

  • Nenad Tomašev
  • Natalie Harris
  • Sebastien Baur
  • Anne Mottram
  • Xavier Glorot
  • Jack William Rae
  • Michal Zielinski
  • Harry Askham
  • Andre Saraiva
  • Valerio Magliulo
  • Clemens Meyer
  • Suman Venkatesh Ravuri
  • Ivan Protsyuk
  • Alistair Connell
  • Cían Hughes
  • Alan Karthikesalingam
  • Julien Cornebise
  • Hugh Montgomery
  • Geraint Rees
  • Christopher Laing
  • Clifton R. Baker
  • Thomas Osborne
  • Ruth Reeves
  • Demis Hassabis
  • Dominic King
  • Mustafa Suleyman
  • Trevor John Back
  • Christopher Nielsen
  • Martin Gamunu Seneviratne
  • Joe Ledsam
  • Shakir Mohamad
Nature Protocols (2021)

Abstract

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks.

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