Development of New Diagnostic Techniques - Machine Learning

Advances in Experimental Medicine and Biology
Delin Sun

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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Addiction

This feed focuses mechanisms underlying addiction and addictive behaviour including heroin and opium dependence, alcohol intoxication, gambling, and tobacco addiction.

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