Digital medicine and the curse of dimensionality.
Digital medicine and the curse of dimensionality.
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DOI：
10.1038/s41746-021-00521-5
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发表时间：
2021-10-28
影响因子：
15.2
通讯作者：
Liss J
中科院分区：
文献类型：
--
作者：
Berisha V;Krantsevich C;Hahn PR;Hahn S;Dasarathy G;Turaga P;Liss J
数字健康数据是多模态和高维的。患者的健康状态可以通过多种信号来表征，包括医学成像、临床变量、基因组测序、临床医生和患者之间的对话以及来自可穿戴设备的连续信号等。这种在患者生活中聚合的大量个性化数据流激发了人们对开发新的人工智能（AI）模型以实现更高精度诊断、预后和跟踪的兴趣。虽然这些算法的前景是不可否认的，但它们的传播和采用一直很缓慢，部分原因是一旦部署在真实的世界中，AI模型的性能就不可预测。我们认为，在开发推广到真实世界场景的算法时，速度限制因素之一是使数据令人兴奋的属性-它们的高维性质。本文考虑了大量数字健康数据中的大量特征如何挑战强大的人工智能模型的发展-这一现象在统计学习理论中被称为“维数灾难”。我们概述了数字健康背景下的维度灾难，展示了它如何对样本外性能产生负面影响，并强调了研究人员和算法设计人员的重要考虑因素。
Digital health data are multimodal and high-dimensional. A patient’s health state can be characterized by a multitude of signals including medical imaging, clinical variables, genome sequencing, conversations between clinicians and patients, and continuous signals from wearables, among others. This high volume, personalized data stream aggregated over patients’ lives has spurred interest in developing new artificial intelligence (AI) models for higher-precision diagnosis, prognosis, and tracking. While the promise of these algorithms is undeniable, their dissemination and adoption have been slow, owing partially to unpredictable AI model performance once deployed in the real world. We posit that one of the rate-limiting factors in developing algorithms that generalize to real-world scenarios is the very attribute that makes the data exciting—their high-dimensional nature. This paper considers how the large number of features in vast digital health data can challenge the development of robust AI models—a phenomenon known as “the curse of dimensionality” in statistical learning theory. We provide an overview of the curse of dimensionality in the context of digital health, demonstrate how it can negatively impact out-of-sample performance, and highlight important considerations for researchers and algorithm designers.
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DOI：
10.1098/rsta.2016.0153
发表时间：
2016-11-13
期刊：
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
影响因子：
--
作者：
Coveney PV;Dougherty ER;Highfield RR
通讯作者：
Highfield RR
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
5.7
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
15.8
作者：
Ioannidis, JPA
通讯作者：
Ioannidis, JPA
影响因子：
3.5
作者：
Figueroa RL;Zeng-Treitler Q;Kandula S;Ngo LH
通讯作者：
Ngo LH
影响因子：
2.9
作者：
DRUCKER, H;CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V