Surgery Duration Prediction Using Multi-Task Feature Selection

David Azriel, Yosef Rinott, Orna Tal, Benyamine Abbou, Nadav Rappoport

Research output: Contribution to journalArticlepeer-review


Efficient optimization of operating room (OR) activity poses a significant challenge for hospital managers due to the complex and risky nature of the environment. The traditional 'one size fits all' approach to OR scheduling is no longer practical, and personalized medicine is required to meet the diverse needs of patients, care providers, medical procedures, and system constraints within limited resources. This paper aims to introduce a scientific and practical tool for predicting surgery durations and improving OR performance for maximum benefit to patients and the hospital. Previous works used machine-learning models for surgery duration prediction based on preoperative data. The models consider covariates known to the medical staff at the time of scheduling the surgery. Given a large number of covariates, model selection becomes crucial, and the number of covariates used for prediction depends on the available sample size. Our proposed approach utilizes multi-task regression to select a common subset of predicting covariates for all tasks with the same sample size while allowing the model's coefficients to vary between them. A regression task can refer to a single surgeon or operation type or the interaction between them. By considering these diverse factors, our method provides an overall more accurate estimation of the surgery durations, and the selected covariates that enter the model may help to identify the resources required for a specific surgery. We found that when the regression tasks were surgeon-based or based on the pair of operation type and surgeon, our suggested approach outperformed the compared baseline suggested in a previous study. However, our approach failed to reach the baseline for an operation-type-based task. By accurately estimating surgery durations, hospital managers can provide care to a greater number of patients, optimize resource allocation and utilization, and reduce waste. This research contributes to the advancement of personalized medicine and provides a valuable tool for improving operational efficiency in the dynamic world of medicine.

Original languageEnglish
Pages (from-to)4216-4223
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number7
Early online date8 Mar 2024
StatePublished - Jul 2024

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Electronic health records (EHR)
  • machine learning
  • operation room (OR)
  • precision medicine
  • prediction model
  • surgery


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