Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, 4849 Calhoun Road, Health & Sciences Bldg 2, Houston, TX, 77204, USA.
BMC Med Res Methodol. 2021 May 6;21(1):96. doi: 10.1186/s12874-021-01284-z.
BACKGROUND: 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: The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.
背景：机器学习（ML）的进步为预测医院再入院提供了很好的机会。本综述综合了美国使用 ML 方法预测医院再入院的文献，并评估了这些方法的性能。
方法：本综述按照系统评价和元分析扩展的首选报告项目（PRISMA-ScR）声明进行。项目的提取也遵循预测模型研究的批判性评价和数据提取（CHARMS）。从 2015 年 1 月 1 日至 2019 年 12 月 10 日，系统地在电子数据库 PUBMED、MEDLINE 和 EMBASE 中进行检索。将文章导入到 COVIDENCE 在线软件中进行标题/摘要筛选和全文资格审查。纳入的研究为使用 ML 技术预测美国患者医院再入院的观察性研究。排除无法获得全文的英文文章。定性综合包括研究特征、使用的 ML 算法和模型验证，定量分析评估了模型性能。使用 R 软件分析了 AUC 方面的模型性能。使用预后研究质量工具（QUIPS）评估了综述研究的质量。
结果：在审查的 522 条引文中有 43 篇符合纳入标准。大多数研究使用电子健康记录（24 项，56%），其次是基于人群的数据来源（15 项，35%）和行政索赔数据（4 项，9%）。最常见的算法是基于树的方法（23 项，53%）、神经网络（NN）（14 项，33%）、正则化逻辑回归（12 项，28%）和支持向量机（SVM）（10 项，23%）。这些研究中大多数（37 项，85%）的质量较高。其中大多数研究（28 项，65%）报告了 AUC 高于 0.70 的 ML 算法。这些研究报告的 AUC 存在一定的变异性，中位数为 0.68（IQR：0.64-0.76；范围：0.50-0.90）。
结论：涉及基于树的方法、NN、正则化逻辑回归和 SVM 的 ML 算法常用于预测美国医院再入院。需要进一步研究来比较 ML 算法在预测医院再入院方面的性能。
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