A Unified Framework on Generalizability of Clinical Prediction Models.
A Unified Framework on Generalizability of Clinical Prediction Models.
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DOI：
10.3389/frai.2022.872720
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发表时间：
2022
影响因子：
4
通讯作者：
Vedula, S. Swaroop
中科院分区：
文献类型：
--
作者：
Wan, Bohua;Caffo, Brian;Vedula, S. Swaroop
关键词：
为了有用，临床预测模型（CPM）必须在新的环境中推广到患者。评估CPM的可推广性有助于识别数据中的虚假关系，提供关于它们何时失败的见解，从而提高CPM的可解释性。在临床研究和机器学习领域中，与CPM的可推广性相关的概念存在不连续性。具体来说，传统的统计原因，解释穷人的普遍性，如模型开发的普遍性，在编码的预测和发展和外部数据集之间的结果，测量误差，无法测量一些预测，和缺失的数据，都有不同的，往往是互补的治疗方法，在这两个领域。目前大多数关于CPM泛化能力的机器学习文献都是关于数据集移位的，其中描述了几种类型。然而，很少有研究存在综合概念在这两个领域。弥合这种概念上的不连续性的背景下的CPM可以促进系统的发展CPM和评价其敏感性的因素，影响概括性。我们从临床研究和机器学习的角度调查了CPM的泛化能力和数据集转移，并描述了一个统一的框架来分析CPM的泛化能力，并解释其对影响因素的敏感性。我们的框架导致了一组信号语句，可以用来表征数据集之间的差异，影响CPM的泛化能力的因素。
To be useful, clinical prediction models (CPMs) must be generalizable to patients in new settings. Evaluating generalizability of CPMs helps identify spurious relationships in data, provides insights on when they fail, and thus, improves the explainability of the CPMs. There are discontinuities in concepts related to generalizability of CPMs in the clinical research and machine learning domains. Specifically, conventional statistical reasons to explain poor generalizability such as inadequate model development for the purposes of generalizability, differences in coding of predictors and outcome between development and external datasets, measurement error, inability to measure some predictors, and missing data, all have differing and often complementary treatments, in the two domains. Much of the current machine learning literature on generalizability of CPMs is in terms of dataset shift of which several types have been described. However, little research exists to synthesize concepts in the two domains. Bridging this conceptual discontinuity in the context of CPMs can facilitate systematic development of CPMs and evaluation of their sensitivity to factors that affect generalizability. We survey generalizability and dataset shift in CPMs from both the clinical research and machine learning perspectives, and describe a unifying framework to analyze generalizability of CPMs and to explain their sensitivity to factors affecting it. Our framework leads to a set of signaling statements that can be used to characterize differences between datasets in terms of factors that affect generalizability of the CPMs.
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DOI：
10.1136/bmj.e5595
发表时间：
2013-02-05
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Hemingway H;Croft P;Perel P;Hayden JA;Abrams K;Timmis A;Briggs A;Udumyan R;Moons KG;Steyerberg EW;Roberts I;Schroter S;Altman DG;Riley RD;PROGRESS Group
通讯作者：
PROGRESS Group
影响因子：
30.8
作者：
Ghassemi, Marzyeh;Oakden-Rayner, Luke;Beam, Andrew L.
通讯作者：
Beam, Andrew L.
影响因子：
7.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
39.2
作者：
Oudega, R;Hoes, AW;Moons, KGM
通讯作者：
Moons, KGM
影响因子：
39.2
作者：
Justice, AC;Covinsky, KE;Berlin, JA
通讯作者：
Berlin, JA