Building an automated, machine learning-enabled platform for predicting post-operative complications

Physiol Meas. 2023 Feb 9;44(2):024001. doi: 10.1088/1361-6579/acb4db.


Objective. In 2019, the University of Florida College of Medicine launched theMySurgeryRiskalgorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record.Approach. This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics.Main Results and Significance. This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.

Keywords: artificial intelligence; clinical decision support; machine learning; post operative complications; surgery.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Electronic Health Records*
  • Forecasting
  • Humans
  • Machine Learning*