Time to reality check the promises of machine learning-powered precision medicine.
Time to reality check the promises of machine learning-powered precision medicine.
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DOI：
10.1016/s2589-7500(20)30200-4
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发表时间：
2020-12
影响因子：
30.8
通讯作者：
Tennant, Peter W. G.
中科院分区：
文献类型：
--
作者：
Wilkinson, Jack;Arnold, Kellyn F.;Murray, Eleanor J.;van Smeden, Maarten;Carr, Kareem;Sippy, Rachel;de Kamps, Marc;Beam, Andrew;Konigorski, Stefan;Lippert, Christoph;Gilthorpe, Mark S.;Tennant, Peter W. G.
机器学习方法与大型电子健康数据库相结合，可以通过改进诊断和预测个体对治疗的反应来实现个性化的医学方法。如果成功，这一策略将代表临床研究和实践的一场革命。然而，尽管个性化医疗的愿景很诱人，但需要区分真正的潜力和炒作。我们认为，个性化医疗的目标面临着严峻的挑战，其中许多挑战无法通过算法复杂性来解决，并呼吁传统方法学家和医疗机器学习专家之间的合作，以避免大量的研究浪费。
Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.
影响因子：
3.4
作者：
Sundström J;Lind L;Nowrouzi S;Lytsy P;Marttala K;Ekman I;Öhagen P;Östlund O
通讯作者：
Östlund O