Predicting active pulmonary tuberculosis using an artificial neural network

Background: Nosocomial outbreaks of tuberculosis (TB) have been attributed to unrecognized pulmonary TB. Accurate assessment in identifying index cases of active TB is essential in preventing transmission of the disease. Objectives: To develop an artificial neural network using clinical and radiographic information to predict active pulmonary TB at the time of presentation at a health-care facility that is superior to physicians' opinion. Design: Nonconcurrent prospective study. Setting: University- affiliated hospital. Participants: A derivation group of 563 isolation episodes and a validation group of 119 isolation episodes. Interventions: A general regression neural network (GRNN) was used to develop the predictive model. Measurements: Predictive accuracy of the neural network compared with clinicians' assessment. Results: Predictive accuracy was assessed by the c- index, which is equivalent to the area under the receiver operating characteristic curve. The GRNN significantly outperformed the physicians' prediction, with calculated c-indices (± SEM) of 0.947 ± 0.028 and 0.61 ± 0.045, respectively (p < 0.001). When the GRNN was applied to the validation group, the corresponding c-indices were 0.923 ± 0.056 and 0.716 ± 0.095, respectively. Conclusion: An artificial neural network can identify patients with active pulmonary TB more accurately than physicians' clinical assessment.