Martin Vincent P, Rouas Jean-Luc, Philip Pierre, Fourneret Pierre, Micoulaud-Franchi Jean-Arthur, Gauld Christophe
Université de Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR5800, Talence, France.
Université de Bordeaux, CNRS, SANPSY, UMR6033, CHU de Bordeaux, Bordeaux, France.
Front Psychiatry. 2022 Jun 9;13:926286. doi: 10.3389/fpsyt.2022.926286. eCollection 2022.
In order to create a dynamic for the psychiatry of the future, bringing together digital technology and clinical practice, we propose in this paper a cross-teaching translational roadmap comparing clinical reasoning with computational reasoning. Based on the relevant literature on clinical ways of thinking, we differentiate the process of clinical judgment into four main stages: collection of variables, theoretical background, construction of the model, and use of the model. We detail, for each step, parallels between: i) clinical reasoning; ii) the ML engineer methodology to build a ML model; iii) and the ML model itself. Such analysis supports the understanding of the empirical practice of each of the disciplines (psychiatry and ML engineering). Thus, ML does not only bring methods to the clinician, but also supports educational issues for clinical practice. Psychiatry can rely on developments in ML reasoning to shed light on its own practice in a clever way. In return, this analysis highlights the importance of subjectivity of the ML engineers and their methodologies.
为了创造未来精神病学的发展动力,将数字技术与临床实践相结合,我们在本文中提出了一条跨学科教学的转化路线图,将临床推理与计算推理进行比较。基于临床思维方式的相关文献,我们将临床判断过程分为四个主要阶段:变量收集、理论背景、模型构建和模型应用。我们详细阐述了每个步骤中以下三者之间的相似之处:i)临床推理;ii)构建机器学习模型的机器学习工程师方法;iii)机器学习模型本身。这种分析有助于理解每个学科(精神病学和机器学习工程)的实证实践。因此,机器学习不仅为临床医生带来了方法,也为临床实践的教育问题提供了支持。精神病学可以借助机器学习推理的发展,以巧妙的方式阐明自身的实践。反过来,这种分析凸显了机器学习工程师及其方法主观性的重要性。