Predicting active pulmonary tuberculosis using an artificial neural network.
Chest
; 116(4): 968-73, 1999 Oct.
Article
en En
| MEDLINE
| ID: mdl-10531161
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.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Admisión del Paciente
/
Tuberculosis Pulmonar
/
Diagnóstico por Computador
/
Redes Neurales de la Computación
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Chest
Año:
1999
Tipo del documento:
Article
País de afiliación:
Estados Unidos