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. 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. 891 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. 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.

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专家与机器——机器学习与大型肝脏手术后专家知情预测结果的比较

机器学习 (ML) 已成功应用于分类任务（例如癌症诊断）。更具挑战性的预测的机器学习性能在很大程度上尚未得到探索。本研究的目的是比较机器学习与专家知情预测对接受大型肝脏手术的患者的手术结果。纳入择期肝手术患者术前参数和术后并发症的单一三级中心数据（2008-2021）。根据两位肝脏外科医生确定的影响术后结果的 14 个参数建立了专家知情的预测模型。ML 模型使用所有可用的术前患者变量 (n = 62)。比较模型性能以预测术后 3 个月的总体发病率。对主要肝切除患者进行了时间验证和附加分析。包括 891 名患者。专家知情模型显示平均偏差较低（2-5 个 CCI 点），而高估/低估则较高。ML 模型的表现类似：平均预测比观察到的 CCI 值高 5-10 个点，且变异性较高（95% CI -30 至 50）。大型肝脏手术患者的表现没有改善。未发现应用 ML 来预测术后总体发病率的临床相关性。尽管机器学习是一种新的炒作，但它具有在临床实践中应用的潜力。然而，现阶段它并没有取代现有的预测建模方法。