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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Published on: October 10, 2018

Machine learning for surgical time prediction.

Oscar Martinez¹, Carol Martinez², Carlos A Parra¹

¹Pontificia Universidad Javeriana, School of Engineering, Bogotá, Colombia.

Computer Methods and Programs in Biomedicine

|June 23, 2021

Summary

This summary is machine-generated.

Machine learning algorithms, particularly Bagged Trees, can accurately predict surgical times, improving operating room efficiency. This approach offers a data-driven alternative to experience-based estimations, potentially reducing hospital costs.

Keywords:

Assembly methods Linear regression Machine learning Regression trees Support vector machine Surgical time prediction

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Area of Science:

Healthcare Operations Research
Applied Machine Learning
Surgical Workflow Optimization

Background:

Operating Rooms (ORs) represent a significant hospital expense.
Inaccurate surgical time estimations lead to increased operational costs.
Current experience-based methods for surgery scheduling can be biased.

Purpose of the Study:

To evaluate machine learning (ML) algorithms for predicting surgical time duration.
To compare the performance of four ML algorithms against experience-based methods.
To optimize operating room efficiency through improved surgical scheduling.

Main Methods:

Applied and compared Linear Regression, Support Vector Machines, Regression Trees, and Bagged Trees.
Utilized historical surgical data from 2004-2019 at a university hospital.
Evaluated algorithms based on Root Mean Square Error (RMSE) and computing time.

Main Results:

All ML algorithms achieved prediction errors between 26-37 minutes.
Bagged Trees demonstrated the best performance with 26 min RMSE and efficient training/testing times.
Bagged Trees outperformed the experience-based method but shifted from overestimation to underestimation.

Conclusions:

Machine learning offers a viable solution for accurate surgical time prediction.
Bagged Trees emerged as the top-performing algorithm for this task.
Further adaptation of ML models is needed to meet specific hospital requirements.