An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions

Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice.We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness.We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups.We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user's preferences.

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Publication Details

Journal
Medical Decision Making
Published
2024-10-23
DOI
https://doi.org/10.1177/0272989x241289336
Citations
1
Primary Topic
Appendicitis Diagnosis and Management
Type
article
Field-Weighted Citation Impact
0.69

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article

An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions

Neil Smart, Silvia Moler Zapata, Ramani Moonesinghe, Robert J. Hinchliffe et al.
1 citations
Medical Decision Making
Appendicitis Diagnosis and Management
0.69
article

An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions

Neil Smart, Silvia Moler Zapata, Ramani Moonesinghe, Robert J. Hinchliffe, Geoff J. Bellingan, Susan Moug, Stephen O’Neill, Andrew Hutchings, Richard Grieve, Ravinder Vohra
article en
1 citations

Abstract

Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice.We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness.We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups.We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user's preferences.

Medical Decision MakingVol. 44(8)
Nottingham University Hospitals NHS Trust (GB), University of Exeter (GB), University of Bristol (GB), London School of Hygiene & Tropical Medicine (GB), Royal Alexandra Hospital (GB), University College London (GB)
London School of Hygiene and Tropical Medicine, National Institute for Health and Care Research, Medical Research Council
Peace, Justice and strong institutions
Openalex Percentile: Top 25%
Appendicitis Diagnosis and Management
0.69
DOIOpen Access PDFOpenAlex
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