Prediction of surgical operation durations using supervised machine learning techniques

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2017

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Zabardast, Ehsan

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There’s an ever increasing number of patients referred to healthcare facilities and hospitals. The healthcare facilities have two main options to deal with this situation. They have to either employ and acquire more resources or they should use the existing staff and resources more efficiently and effectively. The first option is not always feasible due to the fact that the healthcare facilities have limitations on both the staff they can employ and the resources they can acquire. Given the fact that these resources are expensive and extra resources provide diminishing returns, it is important to make the best use of resources available. Operating rooms and surgeons are the most expensive and scarce resources in hospitals; so it is crucial to optimize their performance and avoid under and over utilized operating rooms. The aim of this study is to employ supervised machine learning techniques and probabilistic graphical models to predict the duration of surgical operations using historical data. We have used a wide spectrum of different models ranging from regression methods, classification methods, and Bayesian Networks to predict the surgical operation durations. The models built based on Bayesian Networks, in general, produce more accurate results with lower errors. Naive Bayes, however, outperforms the other Bayesian-Network based models with an average accuracy of 66.9% and root mean square error of 998 seconds (16.6 minutes) from the true duration of the operation. Provided with accurate estimation of surgical operation durations, it is possible to build optimization models to utilize healthcare facility resources. This allows healthcare facilities’ managers to create tactical (medium term) plans and to increase efficient utilization of operating rooms and surgeons.

Subject Keywords

Surgery., Probabilities., Machine learning., Graphic methods.

URI

http://etd.lib.metu.edu.tr/upload/12621247/index.pdf
https://hdl.handle.net/11511/26913

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Graduate School of Informatics, Thesis

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Citation Formats

E. Zabardast, “Prediction of surgical operation durations using supervised machine learning techniques,” M.S. - Master of Science, Middle East Technical University, 2017.