机器学习算法在预测胸部钝挫伤患者机械通气时间延长方面的性能。

IF 2.8 3区 医学 Q1 Pharmacology, Toxicology and Pharmaceutics
Therapeutics and Clinical Risk Management Pub Date : 2024-09-20 eCollection Date: 2024-01-01 DOI:10.2147/TCRM.S482662
Yifei Chen, Xiaoning Lu, Yuefei Zhang, Yang Bao, Yong Li, Bing Zhang
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引用次数: 0

摘要

目的:机械通气(MV)是重症监护室(ICU)收治的钝性胸部创伤(BCT)患者最常用的治疗方法之一。我们的研究旨在探讨机器学习算法在预测 BCT 患者机械通气时间延长(PDMV)方面的性能:在这项单中心观察性研究中,我们选取了通过鼻腔或口腔插管接受 MV 治疗的 BCT 患者。PDMV定义为气管插管后机械通气时间≥7天(正常与延长MV;二分法结果)。采用无监督学习法对原始队列中的数据进行 K-means 聚类。使用多种机器学习算法预测 DMV 类别。通过特征重要性分析确定了最重要的预测因子。最后,开发了基于卡方自动交互检测(CHAID)算法的决策树,以研究临床决策中预测因子的临界点:结果:共纳入 426 名患者和 35 个特征。K-均值聚类将患者分为两组(高风险和低风险)。DMV 分类算法的曲线下面积(AUC)在 0.753 至 0.923 之间。重要度分析表明,肺挫伤体积(VPC)是预测 DMV 的最重要特征。基于 CHAID 的决策树的预测准确率达到了 86.4%:机器学习算法可以预测 BCT 患者的 PDMV。因此,有限的医疗资源可以更合理地分配给有 PDMV 风险的 BCT 患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance of Machine Learning Algorithms in Predicting Prolonged Mechanical Ventilation in Patients with Blunt Chest Trauma.

Purpose: Mechanical ventilation (MV) is one of the most common treatments for patients with blunt chest trauma (BCT) admitted to the intensive care unit (ICU). Our study aimed to investigate the performance of machine learning algorithms in predicting the prolonged duration of mechanical ventilation (PDMV) in patients with BCT.

Methods: In this single-center observational study, patients with BCT who were treated with MV through nasal or oral intubation were selected. PDMV was defined as the duration of mechanical ventilation ≥7 days after endotracheal intubation (normal vs prolonged MV; dichotomous outcomes). K-means was used to cluster data from the original cohort by an unsupervised learning method. Multiple machine learning algorithms were used to predict DMV categories. The most significant predictors were identified by feature importance analysis. Finally, a decision tree based on the chi-square automatic interaction detection (CHAID) algorithm was developed to study the cutoff points of predictors in clinical decision-making.

Results: A total of 426 patients and 35 characteristics were included. K-means clustering divided the cohort into two clusters (high risk and low risk). The area under the curve (AUC) of the DMV classification algorithms ranged from 0.753 to 0.923. The importance analysis showed that the volume of pulmonary contusion (VPC) was the most important feature to predict DMV. The prediction accuracy of the decision tree based on CHAID reached 86.4%.

Conclusion: Machine learning algorithms can predict PDMV in patients with BCT. Therefore, limited medical resources can be more appropriately allocated to BCT patients at risk for PDMV.

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来源期刊
Therapeutics and Clinical Risk Management
Therapeutics and Clinical Risk Management HEALTH CARE SCIENCES & SERVICES-
CiteScore
5.30
自引率
3.60%
发文量
139
审稿时长
16 weeks
期刊介绍: Therapeutics and Clinical Risk Management is an international, peer-reviewed journal of clinical therapeutics and risk management, focusing on concise rapid reporting of clinical studies in all therapeutic areas, outcomes, safety, and programs for the effective, safe, and sustained use of medicines, therapeutic and surgical interventions in all clinical areas. The journal welcomes submissions covering original research, clinical and epidemiological studies, reviews, guidelines, expert opinion and commentary. The journal will consider case reports but only if they make a valuable and original contribution to the literature. As of 18th March 2019, Therapeutics and Clinical Risk Management will no longer consider meta-analyses for publication. The journal does not accept study protocols, animal-based or cell line-based studies.
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