Development of New Diagnostic Techniques - Machine Learning.


Journal Article (Review)

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.

Full Text

Cited Authors

  • Sun, D

Published Date

  • January 2017

Published In

Volume / Issue

  • 1010 /

Start / End Page

  • 203 - 215

PubMed ID

  • 29098674

Pubmed Central ID

  • 29098674

Electronic International Standard Serial Number (EISSN)

  • 2214-8019

International Standard Serial Number (ISSN)

  • 0065-2598

Digital Object Identifier (DOI)

  • 10.1007/978-981-10-5562-1_10


  • eng