Deep Interpretable Early Warning System for the Detection of Clinical Deterioration

Farah E. Shamout, Tingting Zhu, Pulkit Sharma, Peter J. Watkinson, David A. Clifton

Research output: Contribution to journalArticlepeer-review


Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings. We propose the 'Deep Early Warning System' (DEWS), an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission. The model was developed and validated using routinely collected vital signs of patients admitted to the the Oxford University Hospitals between 21st March 2014 and 31st March 2018. We extracted 45 314 vital-sign measurements as a balanced training set and 359 481 vital-sign measurements as an imbalanced testing set to mimic a real-life setting of emergency admissions. DEWS achieved superior accuracy than the state-of-the-art that is currently implemented in clinical settings, the National Early Warning Score, in terms of the overall area under the receiver operating characteristic curve (AUROC) (0.880 vs. 0.866) and when evaluated independently for each of the three outcomes. Our attention-based architecture was able to recognize 'historical' trends in the data that are most correlated with the predicted probability. With high sensitivity, improved clinical utility and increased interpretability, our model can be easily deployed in clinical settings to supplement existing EWS systems.

Original languageEnglish (US)
Article number8844833
Pages (from-to)437-446
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
StatePublished - Feb 2020


  • Early warning system
  • data interpolation
  • deep learning
  • supervised learning
  • time-series data

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


Dive into the research topics of 'Deep Interpretable Early Warning System for the Detection of Clinical Deterioration'. Together they form a unique fingerprint.

Cite this