Computer-assisted Medical Decision-making System for Diagnosis of Urticaria
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....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2016-11-01
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| Series: | MDM Policy & Practice |
| Online Access: | https://doi.org/10.1177/2381468316677752 |
| _version_ | 1811204062279892992 |
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| author | Jabez J. Christopher BE, ME Harichandran Khanna Nehemiah BE, ME, PhD Kannan Arputharaj ME, PhD George L. Moses MSc, PhD |
| author_facet | Jabez J. Christopher BE, ME Harichandran Khanna Nehemiah BE, ME, PhD Kannan Arputharaj ME, PhD George L. Moses MSc, PhD |
| author_sort | Jabez J. Christopher BE, ME |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T03:05:08Z |
| format | Article |
| id | doaj.art-9b33fdb3216e402b9fc85fc46f52ad32 |
| institution | Directory Open Access Journal |
| issn | 2381-4683 |
| language | English |
| last_indexed | 2024-04-12T03:05:08Z |
| publishDate | 2016-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | MDM Policy & Practice |
| spelling | doaj.art-9b33fdb3216e402b9fc85fc46f52ad322022-12-22T03:50:31ZengSAGE PublishingMDM Policy & Practice2381-46832016-11-01110.1177/2381468316677752Computer-assisted Medical Decision-making System for Diagnosis of UrticariaJabez J. Christopher BE, MEHarichandran Khanna Nehemiah BE, ME, PhDKannan Arputharaj ME, PhDGeorge L. Moses MSc, PhDBackground: 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.https://doi.org/10.1177/2381468316677752 |
| spellingShingle | Jabez J. Christopher BE, ME Harichandran Khanna Nehemiah BE, ME, PhD Kannan Arputharaj ME, PhD George L. Moses MSc, PhD Computer-assisted Medical Decision-making System for Diagnosis of Urticaria MDM Policy & Practice |
| title | Computer-assisted Medical Decision-making System for Diagnosis of Urticaria |
| title_full | Computer-assisted Medical Decision-making System for Diagnosis of Urticaria |
| title_fullStr | Computer-assisted Medical Decision-making System for Diagnosis of Urticaria |
| title_full_unstemmed | Computer-assisted Medical Decision-making System for Diagnosis of Urticaria |
| title_short | Computer-assisted Medical Decision-making System for Diagnosis of Urticaria |
| title_sort | computer assisted medical decision making system for diagnosis of urticaria |
| url | https://doi.org/10.1177/2381468316677752 |
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