Gangrenous cholecystitis (GC) is a serious clinical condition associated with high morbidity and mortality rates. Machine learning (ML) has significant potential in addressing the diverse characteristics of real data. We aim to develop an explainable and cost-effective predictive model for GC utilizing ML and Shapley Additive explanation (SHAP) algorithm. This study included a total of 1006 patients with 26 clinical features. Through 5-fold CV, the best performing integrated learning model, XGBoost, was identified. The model was interpreted using SHAP to derive the feature subsets WBC, NLR, D-dimer, Gallbladder width, Fibrinogen, Gallbladder wallness, Hypokalemia or hyponatremia, these subsets comprised the final diagnostic prediction model. The study developed a explainable predictive tool for GC at an early stage. This could assist doctors to make quick surgical intervention decisions and perform surgery on patients with GC as soon as possible. Using clinical data from 1006 cholecystitis patients, we developed a machine learning-based diagnostic prediction model to help identify patients at high risk for acute gangrenous cholecystitis. During the study, the deficiency and imbalance of actual clinical data were directly addressed, leading to the ultimate selection of the integrated learning model XGBoost as the predictive model exhibiting superior performance and stability on a novel, unidentified validation set and compared to preoperative clinical diagnosis. The model employs variables that are non-specific, readily available, reasonably priced, and appropriate for clinical generalization.

中文翻译：

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基于临床数据的可解释坏疽性胆囊炎预测机器学习模型：一项回顾性单中心研究


坏疽性胆囊炎 （GC） 是一种严重的临床疾病，与高发病率和死亡率相关。机器学习 （ML） 在解决真实数据的各种特征方面具有巨大潜力。我们的目标是利用 ML 和 Shapley 加法解释 （SHAP） 算法为 GC 开发一种可解释且具有成本效益的预测模型。本研究共纳入 1006 例患者，具有 26 种临床特征。通过 5 倍 CV，确定了表现最好的综合学习模型 XGBoost。使用 SHAP 对模型进行解释，得出特征子集 WBC 、 NLR 、 D-二聚体 、 胆囊宽度 、纤维蛋白原 、 胆囊壁 、 低钾血症 或 低钠血症，这些子集构成了最终的诊断预测模型。该研究在早期开发了一种可解释的 GC 预测工具。这可以帮助医生快速做出手术干预决定，并尽快对 GC 患者进行手术。使用来自 1006 名胆囊炎患者的临床数据，我们开发了一个基于机器学习的诊断预测模型，以帮助识别急性坏疽性胆囊炎的高危患者。在研究过程中，实际临床数据的不足和不平衡得到了直接解决，导致最终选择了集成学习模型 XGBoost 作为预测模型，该模型在新颖的、未识别的验证集上表现出卓越的性能和稳定性，并与术前临床诊断相比。该模型采用非特异性、容易获得、价格合理且适合临床泛化的变量。