Explainable artificial intelligence model to predict acute critical illness from electronic health records

Nat Commun. 2020 Jul 31;11(1):3852. doi: 10.1038/s41467-020-17431-x.


Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as early warning scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on electronic health records (EHR) trained artificial intelligence (AI) systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. Here, we present an explainable AI early warning score (xAI-EWS) system for early detection of acute critical illness. xAI-EWS potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acute Disease
  • Acute Kidney Injury / blood
  • Acute Kidney Injury / diagnosis*
  • Acute Kidney Injury / pathology
  • Acute Lung Injury / blood
  • Acute Lung Injury / diagnosis*
  • Acute Lung Injury / pathology
  • Area Under Curve
  • Artificial Intelligence*
  • Blood Pressure
  • Critical Illness
  • Early Diagnosis
  • Electronic Health Records / statistics & numerical data*
  • Heart Rate
  • Humans
  • Prognosis
  • ROC Curve
  • Sepsis / blood
  • Sepsis / diagnosis*
  • Sepsis / pathology