Recognition of hypochondriasis in a clinic for sexually transmitted disease.

A descriptive study of 100 consecutive patients referred for psychiatric assessment from a clinic for sexually transmitted disease (STD) is reported. Thirty six patients presented with physical symptoms for which no organic cause could be found. Various physical and psychological features of the overall presentation of this "somatic" group were identified. These are discussed in terms of diagnostic categories, aetiological mechanisms, and theories of illness behaviour. The importance of directly observable aspects of the patients' consultation behaviour is stressed over and above deep psychological constructs. The diagnosis of hypochondriasis is seen as essentially a medical one, which entails the doctor making a set of judgements that require a broad clinical perspective.

Neural network modeling of the level of observation decision in an acute psychiatric ward.

Patients in an acute psychiatric ward need to be observed with varying levels of closeness. We report a series of experiments in which neural networks were trained to model this "level of observation" decision. One hundred eighty-seven such clinical decisions were used to train and test the networks which were evaluated by a multitrial v-fold cross-validation procedure. One neural network modeling approach was to break down the decision process into four subproblems, each of which was solved by a perceptron unit. This resulted in a hierarchical perceptron network having a structure that was equivalent to a sparsely connected two-layer perceptron. Neural network approaches were compared with nearest neighbor, linear regression, and naive Bayes classifiers. The hierarchical and sparse neural networks were the most accurate classifiers. This shows that the decision process is nonlinear, that neural nets can be more accurate than other statistical approaches, and that hierarchical decomposition is a useful methodology for neural network design.