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.