风险评估
预测建模
结果(博弈论)
模型风险
计算机科学
风险分析(工程)
机器学习
数据挖掘
人工智能
风险管理
医学
数学
计算机安全
数理经济学
经济
管理
作者
R Chen,S F Wang,Jing-Wen Zhou,Feng Sun,Wenhua Wei,Siyan Zhan
出处
期刊:PubMed
日期:2020-05-10
卷期号:41 (5): 776-781
被引量:6
标识
DOI:10.3760/cma.j.cn112338-20190805-00580
摘要
This paper introduceds the tool named as "Prediction model Risk Of Bias ASsessment Tool" (PROBAST) to assess the risk of bias and applicability in prediction model studies and the relevant items and steps of assessment. PROBAST is organized into four domains including participants, predictors, outcome and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of risk of bias occurring in study design, conduct or analysis. Through comprehensive judgment, the risk of bias and applicability of original study is categorized as high, low or unclear. PROBAST enables a focused and transparent approach to assessing the risk of bias of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be also used more generally in critical appraisal of prediction model studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI