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Mendeley readers
| Chapter title |
Development of New Diagnostic Techniques – Machine Learning
|
|---|---|
| Chapter number | 10 |
| Book title |
Substance and Non-substance Addiction
|
| Published in |
Advances in experimental medicine and biology, January 2017
|
| DOI | 10.1007/978-981-10-5562-1_10 |
| Pubmed ID | |
| Book ISBNs |
978-9-81-105561-4, 978-9-81-105562-1
|
| Authors |
Delin Sun, Sun, Delin |
| 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. |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 21% |
| Student > Bachelor | 4 | 14% |
| Student > Doctoral Student | 3 | 10% |
| Professor | 2 | 7% |
| Librarian | 2 | 7% |
| Other | 8 | 28% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 38% |
| Computer Science | 5 | 17% |
| Psychology | 4 | 14% |
| Neuroscience | 2 | 7% |
| Nursing and Health Professions | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 3 | 10% |