Couckuyt Artuur, Seurinck Ruth, Emmaneel Annelies, Quintelier Katrien, Novak David, Van Gassen Sofie, Saeys Yvan
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium.
Data Mining and Modeling for Biomedicine, VIB-UGent Center for Inflammation Research, Gent, Belgium.
Hum Genet. 2022 Sep;141(9):1451-1466. doi: 10.1007/s00439-022-02439-8. Epub 2022 Mar 4.
Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as "translational machine learning", joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models. To help clinicians and bioinformaticians refine their translational ML pipelines, we review the steps from model building to the use of ML in the clinic. We discuss experimental setup, computational analysis, interpretability and reproducibility, and emphasize the challenges involved. We highly advise collaboration and data sharing between consortia and institutes to build multi-centric cohorts that facilitate ML methodologies that generalize across centers. In the end, we hope that this review provides a way to streamline translational ML and helps to tackle the challenges that come with it.
机器学习（ML）算法正越来越多地用于帮助实现临床决策支持系统。在这个新领域中，我们将数据科学家和临床医生之间的共同努力与紧密沟通定义为“转化机器学习”，这有助于弥合机器学习与其在临床应用之间的差距。这些合作还提高了对转化机器学习方法的可解释性和信任度，最终目标是生成可推广和可重复的模型。为了帮助临床医生和生物信息学家完善他们的转化机器学习流程，我们回顾了从模型构建到在临床中使用机器学习的各个步骤。我们讨论了实验设置、计算分析、可解释性和可重复性，并强调了其中涉及的挑战。我们强烈建议各联盟和机构之间进行合作与数据共享，以建立多中心队列，促进适用于多个中心的机器学习方法。最后，我们希望这篇综述提供一种简化转化机器学习的方法，并有助于应对随之而来的挑战。
