An Unusual Case of Multiple Food Allergies Comorbid with Multiple Chemical Sensitivity: A Case Report

Multiple Chemical Sensitivity Syndrome: First Symptoms and Evolution of the Clinical Picture: Case-Control Study/Epidemiological Case-Control Study

Multiple chemical sensitivity (MCS) is a chronic condition characterized by the appearance of symptoms caused by exposure to chemical compounds that are tolerable for the general population. It mainly affects middle-aged women. There are very few studies focusing on the most frequent symptoms of MCS considering age groups and gender. The main goal of this study was to find the most frequent symptoms both at the onset of the disease and at the present time describing them by age groups. The QEESI (Quick Environmental Exposure and Sensitivity Inventory) questionnaire, Scale 3 which assesses symptoms and their severity, was used as a diagnostic tool for the disease. A case-control study was conducted with the participation of 210 people. Of the cases, 94.3% were women. The symptoms that most often manifested first were airway and mucous membrane alterations (68.9%). In the development of the disease, we found cognitive alterations (OR = 31.25), heart or chest problems (OR = 22.49), neuromuscular problems (OR = 20.00) and head-related symptomatology (OR = 19.29). Identifying the most frequent pattern of symptoms by age group and sex will allow an early diagnosis of the disease to improve its prognosis and treatment.

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

BACKGROUND

 Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.

OBJECTIVES

 This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.

METHODS

 The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.

RESULTS

 Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.

CONCLUSION

 The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.