A review of recent advances in data analytics for post-operative patient deterioration detection

C.A.J. Petit, R. Bezemer, N.L. Atallah

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)


Most deaths occurring due to a surgical intervention
happen postoperatively rather than during surgery.
The current standard of care in many hospitals cannot fully
cope with detecting and addressing post-surgical deterioration
in time. For millions of patients, this deterioration is
left unnoticed, leading to increased mortality and morbidity.
Postoperative deterioration detection currently relies on
general scores that are not fully able to cater for the complex
post-operative physiology of surgical patients. In the last
decade however, advanced risk and warning scoring techniques
have started to show encouraging results in terms
of using the large amount of data available peri-operatively
to improve postoperative deterioration detection. Relevant
literature has been carefully surveyed to provide a summary
of the most promising approaches as well as how they have
been deployed in the perioperative domain. This work also
aims to highlight the opportunities that lie in personalizing
the models developed for patient deterioration for these
particular post-surgical patients and make the output more
actionable. The integration of pre- and intra-operative data,
e.g. comorbidities, vitals, lab data, and information about
the procedure performed, in post-operative early warning
algorithms would lead to more contextualized, personalized,
and adaptive patient modelling. This, combined with careful integration in the clinical workflow, would result in improved clinical decision support and better post-surgical care outcomes.
Original languageEnglish
Pages (from-to)391-402
Number of pages12
JournalJournal of Clinical Monitoring and Computing
Issue number3
Publication statusPublished - 1 Jun 2018


  • Data analytics
  • Deterioration detection
  • Early warning scores
  • Perioperative care
  • Models, Theoretical
  • Diagnosis, Computer-Assisted
  • Risk Assessment
  • Comorbidity
  • Humans
  • Medical Informatics/methods
  • Postoperative Period
  • Machine Learning
  • Data Collection/methods
  • Data Science
  • Pattern Recognition, Automated
  • Postoperative Complications/diagnosis


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