Using a neural network to screen a population for asthma

PURPOSE: To use a neural network to rank a population according to individual likelihood of asthma based on their responses to a respiratory questionnaire. METHODS: A final diagnosis of asthma can be made only after full clinical assessment but limited resources make it impossible to offer this to complete populations as part of a screening programme. Prioritisation is required so that review can be offered most promptly to those most in need. A stratified random sample of 180 from 6825 respondents to a community survey underwent clinical review. They were categorised according to likelihood of asthma by three independent experts whose opinions were combined into a single probability label for each patient. A neural network was trained to relate questionnaire responses to probability labels. The trained network was applied to the whole community to produce a ranking order based on likelihood of asthma. A screening threshold could then be set to correspond to available resources, and patients above this level with no recorded evidence of asthma diagnosis could be assessed clinically. Using the known probability labels from the training set, it was possible to derive the expected proportion of true asthmatics in any set of patients. RESULTS: If the screening threshold had been set to capture the top 10% of the ranked population (n = 683), then 239 patients above this threshold had no evidence of diagnosis and would need assessment. Of these, it would be expected that 74% would have the diagnosis confirmed. CONCLUSIONS: This approach allows prioritisation of a population where resources for diagnostic examination are limited. Copyright © 2001 Elsevier Science Inc.