Use of a constraint satisfaction network model for the evaluation of the methadone treatments of drug addicts

Imprisoned drug offenders in Taiwan: a gender-based analysis

Using data collected from a 1999 nationwide survey of 700 incarcerated drug users in Taiwan, this article compares gender differences with respect to childhood experiences, family characteristics, drug use patterns, and criminal histories. The results from both bivariate and logistic regression analyses document some gender differences and offer tentative support for feminist views. Overall, female drug offenders in Taiwan were more likely to have spouses with alcohol or drug use-associated problems, experience physical or sexual abuse, grow up in non-two-parent households, and hold temporary and stereotypical female jobs, including prostitution. Compared to men, although female drug offenders reported earlier involvement in criminal activities, they were less violent and had fewer prior arrests. As for sources, women relied on friends or acquaintances to procure their drugs. No gender differences were identified with respect to illicit drug type or duration and frequency of use.

Use of artificial networks in clinical trials: a pilot study to predict responsiveness to donepezil in Alzheimer's disease

OBJECTIVES

To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients.

DESIGN

Convenience sample.

SETTING

Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day.

PARTICIPANTS

Sixty-one older patients of both sexes with AD.

MEASUREMENTS

Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3-month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale-Cognitive portion and Clinician's Interview Based Impression of Change-plus scales.

RESULTS

ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%.

CONCLUSIONS

ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD.

A neural network approach to breast cancer diagnosis as a constraint satisfaction problem

A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84+/-0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs.