Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

机器学习 接收机工作特性 人工智能 校准 随机森林 医学 预测建模 支持向量机 系统回顾 计算机科学 梅德林 统计 数学 政治学 法学
作者
Constanza L. Andaur Navarro,Johanna AAG Damen,Maarten van Smeden,Toshihiko Takada,Steven W J Nijman,Paula Dhiman,Jie Ma,Gary S. Collins,Ram Bajpai,Richard D. Riley,Karel G.M. Moons,Lotty Hooft
出处
期刊:Journal of Clinical Epidemiology [Elsevier BV]
卷期号:154: 8-22 被引量:41
标识
DOI:10.1016/j.jclinepi.2022.11.015
摘要

Background and ObjectivesWe sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.MethodsWe search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes.ResultsWe included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]).ConclusionOur review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models.Systematic review registrationPROSPERO, CRD42019161764.
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