Predicting active pulmonary tuberculosis using an artificial neural network

Abstract

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.