1. ¹Department of Psychiatry, Yu-Li Hospital, Department of Health, Yu-Li, Hualien County, Taiwan
2. ²Division of Mental Health and Substance Abuse Research, National Health Research Institutes, Taipei, Taiwan
3. ³Department of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
4. ⁴Department of Social Medicine, Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
5. ⁵Division of Gerontology Research, National Health Research Institutes, Taipei, Taiwan

Correspondence: Dr H-J Chiu, Yu-Li Hospital, Department of Health, 448 Chung-Hwa Road, Yu-Li, Hualien County, Taiwan. E-mail: tosafish@ms73.hinet.net

Received 19 June 2006; Revised 14 September 2007; Accepted 21 September 2007; Published online 8 January 2008.

Abstract

Artificial intelligence has become a possible solution to resolve the problem of loss of information when complexity of a disease increases. Obesity phenotypes are observable clinical features of drug-naive schizophrenic patients. In addition, atypical antipsychotic medications may cause these unwanted effects. Here we examined the performance of neuro-fuzzy modeling (NFM) in predicting weight changes in chronic schizophrenic patients exposed to antipsychotics. Two hundred and twenty inpatients meeting DSMIV diagnosis of schizophrenia, treated with antipsychotics, either typical or atypical, for more than 2 years, were recruited. All subjects were assessed in the same study period between mid-November 2003 and mid-April 2004. The baseline and first visit's physical data including weight, height and circumference were used in this study. Clinical information (Clinical Global Impression and Life Style Survey) and genotype data of five single nucleotide polymorphisms were also included as predictors. The subjects were randomly assigned into the first group (105 subjects) and second group (115 subjects), and NFM was performed by using the FuzzyTECH 5.54 software package, with a network-type structure constructed in the rule block. A complete learned model trained from merged data of the first and second groups demonstrates that, at a prediction error of 5, 93% subjects with weight gain were identified. Our study suggests that NFM is a feasible prediction tool for obesity in schizophrenic patients exposed to antipsychotics, with further improvements required.