新诊断技术的发展——机器学习。
Development of New Diagnostic Techniques - Machine Learning.
机构信息
Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.
出版信息
Adv Exp Med Biol. 2017;1010:203-215. doi: 10.1007/978-981-10-5562-1_10.
Traditional diagnoses on addiction reply on the patients' self-reports, which are easy to be dampened by false memory or malingering. Machine learning (ML) is a data-driven procedure that learns algorithms from training data and makes predictions. It is quickly developed and is more and more utilized into clinical applications including diagnoses of addiction. This chapter reviewed the basic concepts and processes of ML. Some studies utilizing ML to classify addicts and non-addicts, separate different types of addiction, and evaluate the effects of treatment are also reviewed. Both advantages and shortcomings of ML in diagnoses of addiction are discussed.
传统的成瘾诊断依赖于患者的自我报告，而这些报告容易受到虚假记忆或伪装的影响。机器学习（ML）是一种数据驱动的过程，它从训练数据中学习算法并进行预测。它发展迅速，越来越多地应用于临床应用，包括成瘾的诊断。本章回顾了 ML 的基本概念和过程。还回顾了一些利用 ML 对成瘾者和非成瘾者进行分类、分离不同类型的成瘾以及评估治疗效果的研究。讨论了 ML 在成瘾诊断中的优缺点。
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