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.
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.
artificial neural network can identify patients with active pulmonary
TB more accurately than physicians’ clinical