RESUMEN
In this paper, we present a sequential nonuniform procedure, an inference method which combines feature selection based on the Kullback information gain and a step-wise classification procedure to produce a reliable, interpretable, and robust model. We applied the model to an ovarian tumor data set to distinguish between malignant and benign tumors. The performance of the model was assessed using receiver operating characteristic (ROC) analysis and gave an overall accuracy over 85%, and area under the curve (AUC) of 0.887 which compares well with existing methods. The method presented here is significant because of its ability to handle missing values, and it only uses a small number of variables which are graded according to their discriminative relevance. This, together with the fact that the resulting model is interpretable and has good performance, is likely to lead to widespread clinical acceptance of the method. The method is also generic and can be readily adapted for other classifications problems in biomedicine.
Asunto(s)
Inteligencia Artificial , Sistemas de Apoyo a Decisiones Clínicas , Diagnóstico por Computador/métodos , Modelos Biológicos , Neoplasias Ováricas/diagnóstico , Neoplasias Ováricas/fisiopatología , Cuidados Preoperatorios/métodos , Interpretación Estadística de Datos , Femenino , Humanos , Modelos Estadísticos , Pronóstico , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
In this paper, we present an extension of sequential non-uniform procedure (SNuP) with application of the method to ovarian tumour data, obtained during multicentre study by the International Ovarian Tumour Analysis Group (IOTA). The inference method combines feature selection based on the Kullback information gain and a step-wise classification procedure to produce a reliable, interpretable and robust model. In particular, we extend SNuP to enable it to handle continuous variables without the need for manual specification of thresholds. We applied the extended model to an ovarian tumour data set to distinguish between malignant and benign tumours. The performance of the model was assessed using ROC analysis and gave 86.9% of sensitivity and 84.3% of specificity with overall accuracy level of 84.9%.
Asunto(s)
Algoritmos , Interpretación Estadística de Datos , Sistemas de Apoyo a Decisiones Clínicas , Diagnóstico por Computador/métodos , Neoplasias Ováricas/diagnóstico , Neoplasias Ováricas/cirugía , Análisis Discriminante , Femenino , Humanos , Cuidados Preoperatorios/métodos , Pronóstico , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Resultado del TratamientoRESUMEN
This study examines a novel methodology for continuous fetal heart rate variability (FHRV) assessment in a non-stationary intrapartum fetal heart rate (FHR). The specific aim was to investigate simple statistics, dimension estimates and entropy estimates as methods to discriminate situations of low FHRV related to non-reassuring fetal status or as a consequence of sedatives given to the mother. Using a t-test it is found that the dimension of the zero set and sample entropy reveal a difference in mean distribution of significance >99%. Thus it may prove possible to build a discriminating system based on either one or a combination of these techniques.