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数据管道质量:为医疗保健中的数据驱动算法和人工智能开发和验证质量评估工具

Eris van Twist¹, Brian van Winden², Rogier de Jonge²

¹Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC, Rotterdam, The Netherlands e.vantwist@erasmusmc.nl.

BMJ health & care informatics

|February 2, 2026

概括

一个名为DATA-CARE的新工具使得医疗保健算法的标准化质量评估成为可能. 它发现大多数研究的数据管道质量低至中等,阻碍了临床使用.

科学领域:

医疗保健技术评估医疗保健技术评估
医疗信息学医学信息学
算法验证算法的验证.

背景情况:

数据驱动的算法在医疗保健中越来越多地使用.
缺少这些算法的标准化质量评估工具,特别是它们的数据管道.
现有的工具不能充分解决AI的细微差别和医疗保健中的观察性研究.

研究的目的:

开发和验证用于数据驱动医疗保健算法的标准化质量评估工具.
将评估工具集中在这些算法的关键数据管道上.
解决算法开发中可靠评估的需求.

主要方法:

开发了用于算法批判性评估和强有力的证据的数据评估工具 (DATA-CARE).
DATA-CARE基于预测研究中的质量框架,并审查了10个现有工具.
该工具评估了五个领域:人口,数据,算法,结果和报告透明度,总分为75分.
验证涉及对儿科重症监护室算法的系统审查,有两个独立审查员和ICC分析.

主要成果:

DATA-CARE显示了高的互评分协议 (93.5%) 和0.98.9的ICC.
在验证中,审查了31项研究,其中大多数显示质量低 (32.3%) 或中度 (41.9%).
只有25.8%的研究被评为高质量.

结论:

低到中等质量的研究的流行凸显了在临床实践中实施数据驱动算法的重大障碍.
关键问题包括数据采集/处理不良,验证不充分,报告不透明等.
DATA-CARE提供了一种标准化,可靠的算法评估方法,填补了可重复性研究中的关键空白.

关键词:

人工智能的人工智能是人工智能.数据解释,统计学数据科学数据科学数据科学信号处理,计算机辅助的信号处理.

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