Generation of classification criteria for chronic fatigue syndrome using an artificial neural network and traditional criteria set

In Vivo

Volumn 16, Issue 1, 2002, Pages 37-43

  Linder, Roland ^a   Dinser, R ^a   Wagner, M ^a   Krueger, G R F ^a   Hoffmann, A ^a  

^a UNIVERSITY OF LÜBECK   (Germany)

Author keywords

Artificial neural networks; Chronic fatigue syndrome; Classification criteria

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BODY WEIGHT DISORDER; CENTRAL NERVOUS SYSTEM DISEASE; CHRONIC FATIGUE SYNDROME; CLINICAL TRIAL; COMPARATIVE STUDY; COMPUTER MODEL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DISEASE CLASSIFICATION; FATIGUE; FIBROMYALGIA; GASTROINTESTINAL SYMPTOM; HEART DISEASE; HUMAN; MAJOR CLINICAL STUDY; OUTPATIENT CARE; PARALLEL DESIGN; QUESTIONNAIRE; RANDOMIZED CONTROLLED TRIAL; REGRESSION ANALYSIS; SORE THROAT; SYMPTOM; SYSTEMIC LUPUS ERYTHEMATOSUS; VALIDATION PROCESS;

EID: 0036227985     PISSN: 0258851X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. Chronic fatigue syndrome: A working case definition
2. The chronic fatigue syndrome: A comprehensive approach to its definition and study
3. A report - Chronic fatigue syndrome: Guidelines for research
4. Prevalence of chronic fatigue and chronic fatigue syndrome in a Pacific Northwest health care system
5. Frequency of "chronic active Epstein-Barr infection" in a general medical practice
6. Evidence for impaired activation of the hypothalamic-pituitary-adrenal axis in patients with chronic fatigue syndrome
7. High frequency of fibromyalgia in patients with chronic fatigue seen in a primary care practice
8. Chronic fatigue syndrome and depression: Cause, effect, or covariate
9. Lupus patients with fatigue - Is there a link with fibromyalgia syndrome
10. Primary fibromyalgia and the chronic fatigue syndrome
11. Learning representations by backpropagating errors
12. Statistical approaches to classification: Methods for developing classification and other criteria rules
13. The 1982 revised criteria for the classification of systemic lupus erythematosus
14. The American College of Rheumatology 1990 criteria for the classification of fibromyalgia: Report of the Multi-centre Criteria Committee
15. A novel algorithm to optimize classification trees
16. Dimensionality and sample size considerations in pattern recognition practice
17. Artificial neural networks for medical classification decisions
18. Are artificial neural networks, black boxes?
19. Generating concise and accurate classification rules for breast cancer diagnosis
20. Direct explanations and knowledge extraction from a multilayer perceptron network that pe o s rf rm low back pain classification
21. A note on the G index of agreement