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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
wuy发布了新的文献求助10
1秒前
1秒前
2秒前
sanyecai发布了新的文献求助10
3秒前
3秒前
5秒前
5秒前
Volcano发布了新的文献求助10
6秒前
7秒前
星辰大海应助拓小八采纳,获得10
8秒前
Tao发布了新的文献求助10
9秒前
9秒前
Tangviva1988发布了新的文献求助10
9秒前
阳光沛柔发布了新的文献求助10
10秒前
SYLH应助cyn0762采纳,获得30
11秒前
15秒前
打打应助Tine采纳,获得30
16秒前
研友_VZG7GZ应助EED采纳,获得10
17秒前
缥缈问柳应助wjw采纳,获得10
17秒前
DijiaXu应助朝朝采纳,获得10
19秒前
sanyecai完成签到,获得积分10
19秒前
李健的小迷弟应助高铭泽采纳,获得10
22秒前
Zsl121完成签到,获得积分10
23秒前
23秒前
爆米花应助肖肖采纳,获得10
24秒前
24秒前
冬至完成签到,获得积分10
25秒前
26秒前
shenzhou9完成签到,获得积分10
26秒前
量子星尘发布了新的文献求助10
26秒前
27秒前
lll完成签到,获得积分20
29秒前
欣喜豌豆完成签到,获得积分10
31秒前
彭于晏应助雨过天晴采纳,获得10
31秒前
李健的小迷弟应助唐_采纳,获得10
32秒前
云辞忧发布了新的文献求助10
32秒前
华仔应助Livrik采纳,获得10
32秒前
EED发布了新的文献求助10
33秒前
34秒前
35秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035