Modeling clinical judgment and implicit guideline compliance in the diagnosis of melanomas using machine learning.
We explore several machine learning techniques to model clinical decision making of 6 dermatologists in the clinical task of melanoma diagnosis of 177 pigmented skin lesions (76 malignant, 101 benign). In particular we apply Support Vector Machine (SVM) classifiers to model clinician judgments, Markov Blanket and SVM feature selection to eliminate clinical features that are effectively ignored by the dermatologists, and a novel explanation technique whereby regression tree induction is run on the reduced SVM model's output to explain the physicians' implicit patterns of decision making. Our main findings include: (a) clinician judgments can be accurately predicted, (b) subtle decision making rules are revealed enabling the explanation of differences of opinion among physicians, and (c) physician judgment is non-compliant with the diagnostic guidelines that physicians self-report as guiding their decision making.
Sboner
Andrea
A
ITC-irst and University of Trento, University of Trento, Italy.
Aliferis
Constantin F
CF
D001185:Artificial Intelligence; D003657:Decision Making; D003661:Decision Support Techniques; D003663:Decision Trees; D019983:Guideline Adherence; D006801:Humans; D007600:Judgment; D008545:Melanoma / Q000175:diagnosis*; D010364:Pattern Recognition, Visual; D017410:Practice Guidelines as Topic; D012878:Skin Neoplasms / Q000175:diagnosis*