Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Shuxing Wei, Hongmeng Dong, Weidong Yao, Ying Chen, Xiya Wang, Wenqing Ji, Yongsheng Zhang, Shubin Guo
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Abstract

Background: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU).

Method: Our study utilized data from three distinct sources: the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV databases, and Beijing Chaoyang Hospital. We systematically developed and evaluated 11 distinct machine learning (ML) models, employing a comprehensive set of evaluation metrics to assess model performance, including the area under the curve (AUC). To enhance interpretability and identify key predictive features, we implemented Shapley Additive Explanations (SHAP) analysis for the top-performing model. Furthermore, we developed a streamlined version of the model through strategic feature reduction, followed by rigorous hyperparameter optimization (HPO) to maximize predictive performance. To facilitate clinical implementation, we designed and deployed an intuitive web-based calculator, enabling convenient access and practical application of our optimized predictive model.

Result: The study analyzed 1802 AP patients, with 266 (14.8%) experiencing in-hospital mortality. A set of 27 features was utilized to construct various models, and among them, CatBoost demonstrated the highest performance in both the validation and test sets. To create a more concise model, we selected the top 13 features. After HPO, the AUC in the test set reached 0.835 (95% CI: 0.793-0.872), the AUC in the external validation from Beijing Chaoyang hospital was 0.782 (95% CI: 0.699-0.860).

Conclusion: ML models have shown promising reliability in predicting in-hospital mortality among patients with AP in the ICU. Among these models, the CatBoost model exhibits superior predictive performance, providing valuable assistance to clinical practitioners in identifying high-risk patients and facilitating early interventions to enhance prognosis. The development of a compact model and a web-based calculator further enhances the convenience of using these models in clinical practice.

预测重症监护病房急性胰腺炎住院死亡率的机器学习模型。
背景:急性胰腺炎(AP)是一种危重疾病,及时准确地预测住院死亡率对于指导最佳临床管理至关重要。本研究的重点是开发先进的机器学习(ML)模型,以准确预测入住重症监护病房(ICU)的AP患者的住院死亡率。方法:我们的研究使用了三个不同来源的数据:重症监护医学信息市场III (MIMIC-III)、MIMIC-IV数据库和北京朝阳医院。我们系统地开发和评估了11种不同的机器学习(ML)模型,采用一套全面的评估指标来评估模型的性能,包括曲线下面积(AUC)。为了提高可解释性和识别关键的预测特征,我们对表现最好的模型实施了Shapley加性解释(SHAP)分析。此外,我们通过战略性特征缩减开发了模型的精简版本,然后进行严格的超参数优化(HPO)以最大化预测性能。为了便于临床应用,我们设计并部署了一个直观的基于web的计算器,使我们优化的预测模型能够方便地访问和实际应用。结果:本研究分析了1802例AP患者,其中266例(14.8%)出现院内死亡。我们使用了27个特征来构建各种模型,其中CatBoost在验证集和测试集中都表现出了最高的性能。为了创建一个更简洁的模型,我们选择了前13个特征。HPO后,检验集的AUC达到0.835 (95% CI: 0.793-0.872),北京朝阳医院外部验证的AUC为0.782 (95% CI: 0.699-0.860)。结论:ML模型在预测ICU AP患者住院死亡率方面显示出良好的可靠性。在这些模型中,CatBoost模型表现出优越的预测性能,为临床医生识别高危患者和促进早期干预以提高预后提供了宝贵的帮助。紧凑的模型和基于网络的计算器的发展进一步提高了在临床实践中使用这些模型的便利性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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