A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests

Christopher, Jabez; Nehemiah, H. Khanna; Kannan, A.

doi:10.1016/j.compbiomed.2015.07.019

Cited by 29 publications

(26 citation statements)

References 26 publications

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“…; Jabez Christopher et al. ]. In addition to the diagnostic level of pathogenicity, additional thresholds could be implemented.…”

Section: Applications Of the Pmmentioning

confidence: 99%

How to Define Pathogenicity, Health, and Disease?

Vihinen

2016

Human Mutation

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Scientific and clinical communities produce ever increasing amounts of data and details about health and disease. Our ability to understand and utilize this information is limited because of imprecise language and lack of well-defined concepts. This problem involves also the principal concepts of health, disease, and pathogenicity. Here, a systematic model is presented for pathogenicity, as well as for health and disease. It has three components: extent, modulation, and severity, which jointly define the continuum of pathogenicity. The model is population based, and once implemented, it can be used for numerous purposes such as diagnosis, patient stratification, prognosis, finding phenotype-genotype correlations, or explaining adverse drug reactions. The new model has several benefits including health economy by allowing evidence-based personalized/precision medicine.

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“…; Jabez Christopher et al. ]. In addition to the diagnostic level of pathogenicity, additional thresholds could be implemented.…”

Section: Applications Of the Pmmentioning

confidence: 99%

How to Define Pathogenicity, Health, and Disease?

Vihinen

2016

Human Mutation

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“…of studiesOverall accuracy (%)ApplicationANNSVMBNFLAsthma1882.44 ± 23.71To classify exacerbations [10–14]1121To classify severity [6, 15, 16]––12To classify pathologic vs control [4, 17–19]21–1To classify asthma control level [8, 20]1––1To classify how manage their pathology [21]1–––To predict the clinical effect of salbutamol [22, 23]2–––To classify health state[24]––1–ADR694.5 ± 2.12To predict the posterior probability of a drug (BARDI tool) [28–30]––3–To predict hypersensitivity reaction (AERS database) [25–27]––3–Allergic rhinitis188.31To classify pathologic vs control [31]1…”

Section: Resultsmentioning

confidence: 99%

“…In 2015 Christopher et al [31]. presented a CDSS based on ANN to assist junior clinicians to diagnoses the presence or absence of allergic rhinitis analyzing reports of intradermal skin tests.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, they provide an opportunity to assess the overall information of the main phenomena coming from patients (i.e., identifying information, family history, environmental exposure, perceived treatment efficacy, disease-related quality of life questionnaire) and clinicians (i.e., laboratory tests, values of spirometry), underlying critical features of the disease, treatment planning and the provision of warnings by adding new evidence through associative recall from historical data. In this regard, studies on clinical emergence coupling CDSS-human interaction with the clinician’s knowledge and the suggestions of feedback signals are undertaken in order to generate patient-specific advice and to assist clinicians at the point of care [14, 31, 36]. SC models are also suitable for analyzing data when the likelihood is not defined and statistical tests are not appropriate.…”

Section: Discussionmentioning

confidence: 99%

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The soft computing-based approach to investigate allergic diseases: a systematic review

et al. 2017

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BackgroundEarly recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field.ObjectiveThe aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases.MethodsThe review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016.ResultsThe review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause–effect relationships, which make it difficult to assess the evolution of disease.ConclusionsAlthough most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.

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“…Computer-assisted systems are used for diagnosis, decision making, and decision support in various medical applications such as cancer care, 10,11 heart disease diagnosis, 12 thrombosis diagnosis, 13 diagnosis and treatment of lung disorders, 14,15 drug reaction analysis, 16 and allergy diagnosis. 17 …”

Section: Introductionmentioning

confidence: 99%

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

Christopher

Nehemiah

Kannan

et al. 2016

MDM Policy & Practice

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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.

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A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests

Cited by 29 publications

References 26 publications

How to Define Pathogenicity, Health, and Disease?

How to Define Pathogenicity, Health, and Disease?

The soft computing-based approach to investigate allergic diseases: a systematic review

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

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