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Development of New Diagnostic Techniques – Machine Learning

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Substance and Non-substance Addiction

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1010))

Abstract

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.

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Author information

Authors and Affiliations

  1. Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA

    Delin Sun

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  1. Delin Sun
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Correspondence to Delin Sun .

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Editors and Affiliations

  1. Key Laboratory of Brain Function and Disease, Chinese Academy of Sciences, and School of Life Sciences, University of Science & Technology of China, Hefei, Anhui, China

    Xiaochu Zhang

  2. National Institute on Drug Dependence, Peking University, Beijing, China

    Jie Shi

  3. Department of Psychological Medicine, PLA Army General Hospital, Beijing, China

    Ran Tao

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Sun, D. (2017). Development of New Diagnostic Techniques – Machine Learning. In: Zhang, X., Shi, J., Tao, R. (eds) Substance and Non-substance Addiction. Advances in Experimental Medicine and Biology, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-10-5562-1_10

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