Experts vs. machine - comparison of machine learning to expert-informed prediction of outcome after major liver surgery.

Autor:	Staiger RD; Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland. Electronic address: roxane.staiger@luks.ch., Mehra T; Department of Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland., Haile SR; Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland., Domenghino A; Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland., Kümmerli C; Department of Surgery, Clarunis University Hospital, Basel, Switzerland., Abbassi F; Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland., Kozbur D; Department of Economics, University of Zurich, Zurich, Switzerland., Dutkowski P; Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland., Puhan MA; Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland., Clavien PA; Department of Surgery & Transplantation, University Hospital Zurich, Zurich, Switzerland.
Jazyk:	angličtina
Zdroj:	HPB : the official journal of the International Hepato Pancreato Biliary Association [HPB (Oxford)] 2024 May; Vol. 26 (5), pp. 674-681. Date of Electronic Publication: 2024 Feb 13.
DOI:	10.1016/j.hpb.2024.02.006
Abstrakt:	Background: Machine learning (ML) has been successfully implemented for classification tasks (e.g., cancer diagnosis). ML performance for more challenging predictions is largely unexplored. This study's objective was to compare machine learning vs. expert-informed predictions for surgical outcome in patients undergoing major liver surgery. Methods: Single tertiary center data on preoperative parameters and postoperative complications for elective hepatic surgery patients were included (2008-2021). Expert-informed prediction models were established on 14 parameters identified by two expert liver surgeons to impact on postoperative outcome. ML models used all available preoperative patient variables (n = 62). Model performance was compared for predicting 3-month postoperative overall morbidity. Temporal validation and additional analysis in major liver resection patients were conducted. Results: 889 patients included. Expert-informed models showed low average bias (2-5 CCI points) with high over/underprediction. ML models performed similarly: average prediction 5-10 points higher than observed CCI values with high variability (95% CI -30 to 50). No performance improvement for major liver surgery patients. Conclusion: No clinical relevance in the application of ML for predicting postoperative overall morbidity was found. Despite being a novel hype, ML has the potential for application in clinical practice. However, at this stage it does not replace established approaches of prediction modelling. (Copyright © 2024 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)
Databáze:	MEDLINE
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