Computer-assisted Medical Decision-making System for Diagnosis of Urticaria

Abstract

Background: Urticaria is a common allergic disease that affects all age groups. Allergic disorders are diagnosed at allergy testing centers using skin tests. Though skin tests are the gold standard tests for allergy diagnosis, specialists are required to interpret the observations and test results. Hence, a computer-assisted medical decision-making (CMD) system can be used as an aid for decision support, by junior clinicians, in order to diagnose the presence of urticaria. Methods: The data from intradermal skin test results of 778 patients, who exhibited allergic symptoms, are considered for this study. Based on food habits and the history of a patient, 40 relevant allergens are tested. Allergen extracts are used for skin test. Ten independent runs of 10-fold cross-validation are used to train the system. The performance of the CMD system is evaluated using a set of test samples. The test samples were also presented to the junior clinicians at the allergy testing center to diagnose the presence or absence of urticaria. Results: From a set of 91 features, a subset of 41 relevant features is chosen based on the relevance score of the feature selection algorithm. The Bayes classification approach achieves a classification accuracy of 96.92% over the test samples. The junior clinicians were able to classify the test samples with an average accuracy of 75.68%. Conclusion: A probabilistic classification approach is used for identifying the presence or absence of urticaria based on intradermal skin test results. In the absence of an allergy specialist, the CDM system assists junior clinicians in clinical decision making.

COVID‐19 and artificial intelligence: Experts and dermatologists perspective

“… 20 , 21 Additionally ML techniques for diagnosing urticarial lesions by junior physicians have been studied. 22 Frequent hand washing, use of alcohol‐based sanitizers and gloves have been advocated to combat transmission of COVID‐19. These practices have led to a new range of dermatological problems including hand eczema, contact dermatitis, and glove latex allergy.…”

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

“…The direction of individual krill is maintained by target effect, local effect, and repulsive effect. The induced movement N i by other krills is calculated using Equations (8) and (9).…”

“…Knowledge mining using rough sets for feature selection and backpropagation neural network (BPNN) for classifying clinical datasets has been proposed in [7]. A CDSS to diagnose Urticaria using Bayes classification is proposed in [8]. CDSSs to diagnose lung disorders are proposed in [9][10][11][12][13][14].…”

The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

“…Several other tools have been developed to digitally augment allergy care namely supporting diagnosis and management in a primary care setting ( 58 ) or among junior clinicians ( 59 ), as well as for specific allergic diseases such as drug hypersensitivity ( 60 – 63 ), food allergies ( 64 ) and urticaria ( 65 ).…”