Scoring systems in dermatology

Three scoring systems were considered and described for dermatological applications in which malignant and benign skin lesions have to be recognized: two models are derived from logistic regression and naïve Bayes rule by rounding model parameters to their nearest integer values; the third approach defines the scoring system by a direct stepwise adding of the most significant binary risk factors. An application example of a direct score model was then developed to illustrate important aspects of its design in dermoscopy. The results show that, having many variables available, score models combine simplicity, practicality, high accuracy and good control of overfitting. Also they can incorporate different diagnostic styles of experienced dermatologists, introducing into the model subjective binary variables, some of which also assessed with a significant degree of disagreement.

[1]  Constantine A Gatsonis,et al.  Wilcoxon-based group sequential designs for comparison of areas under two correlated ROC curves. , 2008, Statistics in medicine.

[2]  Emanuela Barbini Md Medical Doctor.  A naïve approach for deriving scoring systems to support clinical decision making , 2013 .

[3]  David J. Hand,et al.  ROC Curves for Continuous Data , 2009 .

[5]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[6]  J. Vincent,et al.  Clinical review: Scoring systems in the critically ill , 2010, Critical care.

[7]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[8]  Lucila Ohno-Machado,et al.  The use of receiver operating characteristic curves in biomedical informatics , 2005, J. Biomed. Informatics.

[9]  J Carpenter,et al.  Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. , 2000, Statistics in medicine.

[10]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[11]  R Hofmann-Wellenhof,et al.  A simple scoring system for the diagnosis of palmo‐plantar pigmented skin lesions by digital dermoscopy analysis , 2013, Journal of the European Academy of Dermatology and Venereology : JEADV.

[12]  Walter Krämer.  Wojtek J. Krzanowski and David J. Hand: ROC curves for continuous data , 2011 .

[13]  L. Joseph,et al.  Bayesian Statistics: An Introduction , 1989 .

[14]  P. Barbini,et al.  Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study. , 2002, The Journal of investigative dermatology.

[15]  B. Efron,et al.  Bootstrap confidence intervals , 1996 .

[16]  A Agresti,et al.  On Logit Confidence Intervals for the Odds Ratio with Small Samples , 1999, Biometrics.

[17]  Paolo Barbini,et al.  Design of Scoring Models for Trustworthy Risk Prediction in Critical Patients , 2011 .

[18]  Paolo Barbini,et al.  A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system , 2010, BMC Medical Informatics Decis. Mak..

[19]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[20]  Riccardo Bellazzi,et al.  A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays , 2006, BMC Bioinformatics.