Machine learning approach and oral food challenge with heated egg

Kuniyoshi, Yasutaka; Tokutake, Haruka; Takahashi, Natsuki; Kamura, Azusa; Yasuda, Sumie; Tashiro, M.

doi:10.1111/pai.13433

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…This study can be viewed in part as validating the prior work from our group in Zhang et al 11 and also as demonstrating how machine learning models trained on one FA data set may be made readily applicable to other data sets. We are aware of a similar report of machine learning used in cooked egg allergy 30 ; that study is notable as an analysis of a clinical retrospective cooked egg challenge cohort, although the predictive power of the machine learning model was relatively low, perhaps partly because of the total number of OFCs analyzed (n = 67). Machine learning has been applied in other FA contexts.…”

Section: Discussionmentioning

confidence: 99%

Prediction of pediatric peanut oral food challenge outcomes using machine learning

Gryak,

Georgievska,

Zhang

et al. 2024

Journal of Allergy and Clinical Immunology: Global

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Section: Discussionmentioning

confidence: 99%

Prediction of pediatric peanut oral food challenge outcomes using machine learning

Gryak,

Georgievska,

Zhang

et al. 2024

Journal of Allergy and Clinical Immunology: Global

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“… 5 21 28 29 ML was first utilized for predicting outcomes of the heated egg challenge, and a recent study with ensemble learning developed a prediction model for peanut, egg, and milk allergies. 13 14 Nevertheless, the aforementioned studies developed models for diagnosing food allergies. In real-world clinical settings, OFCs are also performed for the confirmation of tolerance development.…”

Section: Discussionmentioning

confidence: 99%

“… 11 12 In food allergies, studies have utilized ML to predict outcomes of milk, egg, peanut allergies held for diagnostic purposes. 13 14 ML enables identifying and selecting key variables from intricate datasets, a process sometimes too complex for traditional statistical methods. Factors that influence the passage of OFCs for tolerance confirmation are more intricate than those for diagnosis, and ML’s feature ranking techniques emerge as appropriate tools for an effective analysis.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Outcome of Pediatric Oral Food Challenges for Determining Tolerance Development

Kim,

Jeong,

Park

et al. 2024

Allergy Asthma Immunol Res

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Purpose Despite the risk of anaphylaxis, oral food challenges (OFCs) are performed clinically for various indications, particularly to confirm tolerance development. This study aimed to assess OFCs by relevant indications and build an outcome prediction model to help determine when to perform OFCs in children who are likely to have developed immune tolerance. Methods In total, 432 pediatric OFCs were retrospectively analyzed according to indications. Clinical characteristics, serum total immunoglobulin (Ig) E, blood eosinophils, and specific IgE and IgG4 levels for food allergens were noted and compared. Machine learning was utilized to select the most important variables in determining the passage of the OFCs, and prediction models were constructed using the selected variables. Results OFCs were most commonly performed to confirm tolerance development (number, %; 267, 61.8%). The most common food allergens tested were egg (191, 44.2%) and milk (135, 31.3%). Children who passed the egg challenges for confirming tolerance acquisition had significantly lower egg white-specific IgE level ( P = 0.008). Similarly, those who passed milk challenges had significantly lower cow’s milk-specific IgE ( P = 0.002) and casein-specific IgE levels ( P = 0.005). We developed a nomogram to predict the outcome of OFCs to determine the tolerance acquisition with the selected variables; lower food-specific IgE, higher total IgE, and younger age indicated a higher probability of passage. The area under the curve (95% confidence interval) was 0.623 (0.503–0.743) for egg and 0.734 (0.628–0.840) for milk. Conclusions Serum total IgE and food-specific IgE combined with age showed trends toward passing OFCs for confirming tolerance development. The constructed model may be used by clinicians as a practical guide for minimizing the risks of OFCs and a timely reintroduction for children with food allergies.

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“…144,145 AI's capabilities also extend to predicting disease sub-phenotypes in food-allergic patients, aiding in patient stratification into distinct disease or exposure subgroups, risk stratification, cluster analysis and biomarker identification. [146][147][148] A limited number of studies have investigated AIT outcomes.…”

Section: Advan Ced Data Manag Ement: B I G Data and Artifi Cial Intel...mentioning

confidence: 99%

Hot topics in allergen immunotherapy, 2023: Current status and future perspective

Zemelka‐Wiacek,

Agache,

Akdis

et al. 2023

Allergy

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The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality‐of‐life improvements and cost‐effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen‐specific T‐ and B‐cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen‐specific IgE and increased IgG1 and IgG4, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non‐omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real‐world evidence, allow more flexibility and cost reduction. The analyses of AIT cost‐effectiveness show a clear long‐term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.

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Machine learning approach and oral food challenge with heated egg

Abstract: al. Prevalence of challenge-proven IgE-mediated food allergy using population-based sampling and predetermined challenge criteria in infants.

Cited by 10 publications

References 10 publications

Prediction of pediatric peanut oral food challenge outcomes using machine learning

Prediction of pediatric peanut oral food challenge outcomes using machine learning

Predicting the Outcome of Pediatric Oral Food Challenges for Determining Tolerance Development

Hot topics in allergen immunotherapy, 2023: Current status and future perspective

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