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与人工智能的比较如何揭示临床医生的推理方式?一种交叉视角。

How Does Comparison With Artificial Intelligence Shed Light on the Way Clinicians Reason? A Cross-Talk Perspective.

作者信息

Martin Vincent P, Rouas Jean-Luc, Philip Pierre, Fourneret Pierre, Micoulaud-Franchi Jean-Arthur, Gauld Christophe

机构信息

Université de Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR5800, Talence, France.

Université de Bordeaux, CNRS, SANPSY, UMR6033, CHU de Bordeaux, Bordeaux, France.

出版信息

Front Psychiatry. 2022 Jun 9;13:926286. doi: 10.3389/fpsyt.2022.926286. eCollection 2022.

DOI:10.3389/fpsyt.2022.926286
PMID:35757203
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9218339/
Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d51/9218339/a80fdfc96470/fpsyt-13-926286-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d51/9218339/a80fdfc96470/fpsyt-13-926286-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d51/9218339/a80fdfc96470/fpsyt-13-926286-g0001.jpg

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本文引用的文献

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Artificial Intelligence and Statistics: Just the Old Wine in New Wineskins?人工智能与统计学:不过是新瓶装旧酒?
Front Digit Health. 2022 Jan 26;4:833912. doi: 10.3389/fdgth.2022.833912. eCollection 2022.
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How to Design a Relevant Corpus for Sleepiness Detection Through Voice?
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Front Digit Health. 2021 Sep 22;3:686068. doi: 10.3389/fdgth.2021.686068. eCollection 2021.
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The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality.数字精神病学的发展领域:当前证据以及应用程序、社交媒体、聊天机器人和虚拟现实的未来。
World Psychiatry. 2021 Oct;20(3):318-335. doi: 10.1002/wps.20883.
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Integrating clinical staging and phenomenological psychopathology to add depth, nuance, and utility to clinical phenotyping: a heuristic challenge.将临床分期与现象学精神病理学相结合,为临床表型学增添深度、细微差别和实用性:一种启发式挑战。
Lancet Psychiatry. 2021 Feb;8(2):162-168. doi: 10.1016/S2215-0366(20)30316-3. Epub 2020 Nov 19.
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