Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data
This work addresses the challenge of improving deep learning-based Lyme disease classifiers by incorporating patient data, but it is incremental as it builds on existing image-based methods without demonstrating new SOTA results.
The study tackled the problem of diagnosing Lyme disease's erythema migrans skin lesions by eliciting expert opinions from doctors to generate probability scores from patient data, which can assist deep learning models, though no concrete performance numbers are provided.
Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Physicians rely on patient information about the background of the skin lesion to confirm their diagnosis. In order to assist the deep learning model with a probability score calculated from patient data, this study elicited opinion from fifteen doctors. For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors evaluations to probability scores using Gaussian mixture based density estimation. For elicited probability model validation, we exploited formal concept analysis and decision tree. The elicited probability scores can be utilized to make image based deep learning Lyme disease pre-scanners robust.