Backpropagation and adaptive resonance theory in predicting suicidal risk

Medical Informatics

Volumn 23, Issue 4, 1998, Pages 325-330

  Modai, I ^a,d   Greenstain, S ^b   Weizman, A ^a,c   Mendel, S ^a  

^a TEL AVIV UNIVERSITY   (Israel)

^b GEHA MENTAL HEALTH CENTER   (Israel)

^c RABIN MEDICAL CENTER   (Israel)

^d SHA'AR MENASHE MENTAL HEALTH CENTER   (Israel)

Author keywords

Adaptive resonance theory; Backpropagation; Neural networks; Suicide prediction

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; DEMOGRAPHY; HOSPITALIZATION; HUMAN; ISRAEL; MAJOR CLINICAL STUDY; MEDICAL INFORMATION; MEDICAL RECORD; MENTAL PATIENT; MULTICENTER STUDY; PREDICTION; PROBABILITY; RELIABILITY; RISK ASSESSMENT; SCREENING; STATISTICAL ANALYSIS; SUICIDE; SUICIDE ATTEMPT;

EID: 84860408465     PISSN: 03077640     EISSN: None     Source Type: Journal    
DOI: 10.3109/14639239809025368     Document Type: Article

References (15)

1. KAPLAN, H. I., SADOCK, B. J., and GREBB, A., 1994, Synopsis of Psychiatry (Baltimore: Williams & Wilkins).
2. MOTTO, J. A., HEILBORN, D. C., and JUSTER, R. P., 1985, Development of a clinical instrument to estimate suicide risk. American Journal of Psychiatry, 14, 680-686.
3. PALLIS, D. J., BARRACLOUGH, B. M., LEVY, A. B., JENKINS, J. S., and SAINSBURY, P., 1982, Estimating risk among attempted suicides : the development of new clinical scales. British Journal of Psychiatry, 141, 37-44.
4. DAVIS, A. T., and SCHRUEDER, C., 1990, The prediction of suicide. The Medical Journal of Australia, 153, 552-554.
5. POKORNY, A. D., 1983, Prediction of suicide in psychiatric patients. Archives of General Psychiatry, 40, 249-257.
6. WASSERMAN, P. D., 1989, Neural Computing-Theory and Practice (New York: Van Nostard Reinhold, ANZA Research Inclinations).
7. GARSON, G. D., (1991) Interpreting neural network connecting weights. Artificial Intelligence Expert : the Magazine of Artificial Intelligence in Practice, 4, 47-51.
8. FREEMAN, J. A., and SKAPURA, D. M., 1992, Neural Networks - Algorithms, Applications and Programming Techniques (New York: Addison Wesley Publishing Company Inc.), pp. 291-336.
9. BAXT, W. G., 1991, Use of an artificial neural network for the diagnosis of myocardial infarction. Annals of Internal Medicine, 115, 843-848.
10. MODAI, I., STOLER, M., and INBAR-SABAN, N., and SABAN, N., 1993, Clinical decisions for psychiatric inpatients and their evaluation by a trained neural network. Methods of Information in Medicine, 32, 396-399.
11. MODAI, I., INBAR-SABAN, N., STOLER, M., VALEVSKI, A., and SABAN N., 1995, Sensitivity profile of 41 psychiatric parameters determined by neural networks in relation to 8 week outcome. Computers in Human Behaviour, 11, 181-190.
12. BOONE, J. M., SIGILLITO, V. G., and SHABER, G. S., 1990, Neural network in radiology: an introduction and evaluation in a signal detection task. Medical Physics, 17, 234-241.
13. MANN, N. H., and BROWN, M. D., 1991, Artificial intelligence in the diagnosis of low back pain. Orthopedic Clinics of North America, 22, 303-314.
14. MODAI, I., ISRAEL, A., MENDEL, S., HINES, E. H., and WEIZMAN, R., 1997, Neural network based on adaptive resonance theory as compared to experts in suggesting treatment for schizophrenic and unipolar depressed inpatients. Journal of Medical Systems, 20, 403-412..
15. ELLIOTT, A. J., PAGES, K. P., RUSSO, J., WILSON, L. G., and ROY-BYRNE, P. P., 1996, A profile of medically serious suicide attempts. Journal of Clinical Psychiatry, 57, 567-571.