Key result
MySurgeryRisk algorithm predicts eight major post-operative complications using automated EHR data.
Why the study?
To build an efficient and accurate machine learning-enabled model for data processing and predicting eight major post-operative complications using automatically extracted electronic health record data.
Describes the development and implementation of a machine learning platform for predicting post-operative complications using EHR data.
May enable automated EHR-based risk stratification; leaves open prospective validation before clinical use.
Objective . In 2019, the University of Florida College of Medicine launched the MySurgeryRisk algorithm 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.
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Balch et al. (2023) studied Post-operative complications. MySurgeryRisk algorithm was evaluated on Prediction of eight major post-operative complications. The MySurgeryRisk algorithm was developed to predict eight major post-operative complications using automatically extracted electronic health record data, including fixed and time-series measurements.
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