SCI晨读：机器学习在预测再入院率中的应用：范围综述

SCI晨读：
机器学习在预测再入院率中的应用：范围综述

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【SCI晨读：机器学习在预测再入院率中的应用：范围综述】

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摘要背景
机器学习 (ML) 的进步为预测再入院提供了巨大的机会。本综述综合了有关 ML 方法及其在美国预测再入院率性能的文献。方法
该综述根据PRISMA-ScR进行。根据预测建模研究系统评价的批判性评估和数据提取 (CHARMS) 指导进行项目提取。系统检索 PUBMED、MEDLINE 和 EMBASE中 2015 年 1 月 1 日至 2019 年 12 月 10 日的文献。将文献导入 COVIDENCE 在线软件进行标题/摘要筛选和全文筛选。纳入标准为使用 ML 技术关注美国患者再入院的观察性研究。排除无英文全文的文献。定性综合的内容包括研究特征、使用的 ML 算法和模型验证，对评估模型性能进行定量分析。使用 R 软件分析曲线下面积 (AUC) 方面的模型性能。使用预后研究质量 (QUIPS) 工具评估研究的质量。结果
在检索的 522 篇文献中， 43 项研究符合纳入标准。多数研究使用电子健康记录 (24, 56%) ，其次是基于人群的数据源 (15, 35%) 和行政索赔数据 (4, 9%) 。最常见的算法是基于树的方法 (23, 53%)、神经网络 (NN) (14, 33%)、正则逻辑回归 (12, 28%) 和支持向量机 (SVM) (10, 23%) ）。多数研究 (37, 85%) 都是高质量的。多数研究 (28, 65%) 报告了 AUC 高于 0.70 的 ML 算法。这些研究报告的 AUC 中存在一系列变异性，中位数为 0.68（IQR：0.64-0.76；范围：0.50-0.90）。结论
通常用于预测美国的再入院率的方法包括基于树的方法、NN、正则逻辑回归和 SVM 的 ML 算法。需要进一步研究来比较 ML 算法在医院再入院预测中的性能。
AbstractBackground
Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US.Methods
This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies.Results
Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64–0.76; range: 0.50–0.90).Conclusions