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引用次数: 0
摘要
机器学习(ML)算法在医学中的应用引发了激烈的讨论。它被认为是几十年来最具颠覆性的通用技术之一。它已经渗透到我们日常生活的许多领域,并产生了我们再也离不开的应用,如导航应用程序或翻译软件。然而,许多人仍然不确定是否应该以目前的形式将 ML 算法应用于医学领域。医生们怀疑他们在多大程度上可以相信算法的预测。开发过程中的缺陷和不明确的监管会导致偏见、不平等、适用性问题和不透明的评估。然而,过去的失误让我们更好地了解了开发有效临床应用模型所需的条件。医生和临床研究人员必须参与所有开发阶段并了解其陷阱。在这篇综述中,我们将解释 ML 的基本概念,介绍血栓与止血领域的实例,讨论常见的陷阱,并提出一个可用于开发有效算法的方法论框架。
Machine-Learning Applications in Thrombosis and Hemostasis.
The use of machine-learning (ML) algorithms in medicine has sparked a heated discussion. It is considered one of the most disruptive general-purpose technologies in decades. It has already permeated many areas of our daily lives and produced applications that we can no longer do without, such as navigation apps or translation software. However, many people are still unsure if ML algorithms should be used in medicine in their current form. Doctors are doubtful to what extent they can trust the predictions of algorithms. Shortcomings in development and unclear regulatory oversight can lead to bias, inequality, applicability concerns, and nontransparent assessments. Past mistakes, however, have led to a better understanding of what is needed to develop effective models for clinical use. Physicians and clinical researchers must participate in all development phases and understand their pitfalls. In this review, we explain the basic concepts of ML, present examples in the field of thrombosis and hemostasis, discuss common pitfalls, and present a methodological framework that can be used to develop effective algorithms.
期刊介绍:
Hämostaseologie is an interdisciplinary specialist journal on the complex topics of haemorrhages and thromboembolism and is aimed not only at haematologists, but also at a wide range of specialists from clinic and practice. The readership consequently includes both specialists for internal medicine as well as for surgical diseases.