@文章{info:doi/ 10.2199 /38082，作者=“李纪力和刘，思如和胡，云迪和朱，凌峰和毛，余佳和刘佳林”，标题=“使用可解释的机器学习模型预测重症监护室心力衰竭患者的死亡率:回顾性队列研究”，期刊=“J医学互联网研究”，年=“2022”，月=“8”，日=“9”，卷=“24”，数=“8”，页=“e38082”，关键词=“心力衰竭;死亡率;重症监护室;预测;XGBoost;世鹏科技电子;背景:心力衰竭(HF)是一种常见病，也是一种重大的公共卫生问题。心衰死亡率预测对于制定个性化的预防和治疗方案至关重要。然而，由于缺乏可解释性，大多数心衰死亡率预测模型尚未达到临床应用。目的:我们旨在开发一个可说明的模型来预测心力衰竭患者的死亡率重症监护病房(icu)和利用夏普利添加剂解释(世鹏科技电子)方法来解释极端梯度增加(XGBoost)模型,探讨心力衰竭的预后因素。 Methods: In this retrospective cohort study, we achieved model development and performance comparison on the eICU Collaborative Research Database (eICU-CRD). We extracted data during the first 24 hours of each ICU admission, and the data set was randomly divided, with 70{\%} used for model training and 30{\%} used for model validation. The prediction performance of the XGBoost model was compared with three other machine learning models by the area under the curve. We used the SHAP method to explain the XGBoost model. Results: A total of 2798 eligible patients with HF were included in the final cohort for this study. The observed in-hospital mortality of patients with HF was 9.97{\%}. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.824 (95{\%} CI 0.7766-0.8708), whereas support vector machine had the poorest generalization ability (AUC=0.701, 95{\%} CI 0.6433-0.7582). The decision curve showed that the net benefit of the XGBoost model surpassed those of other machine learning models at 10{\%}{\textasciitilde}28{\%} threshold probabilities. The SHAP method reveals the top 20 predictors of HF according to the importance ranking, and the average of the blood urea nitrogen was recognized as the most important predictor variable. Conclusions: The interpretable predictive model helps physicians more accurately predict the mortality risk in ICU patients with HF, and therefore, provides better treatment plans and optimal resource allocation for their patients. In addition, the interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians. ", issn="1438-8871", doi="10.2196/38082", url="//www.mybigtv.com/2022/8/e38082", url="https://doi.org/10.2196/38082", url="http://www.ncbi.nlm.nih.gov/pubmed/35943767" }