Allergen chip (AC) technologies are a powerful tool for simultaneous analysis of hundreds of allergens, generating a comprehensive sensitization landscape for precision medicine in allergy. These considerable data require extensive knowledge for translation into clinically relevant conclusion.

To harness machine learning for AC interpretation in daily practice, we set out to establish a nationwide open database of AC, demographic, and clinical information and to submit it to an international crowdsourced machine learning competition to generate a predictive allergy classification algorithm.

The project consortium defined 20 clinical variables and 5 demographic factors for retrospective collection in conjunction with AC IgE data (2014-23) from 11 French university hospitals. The dataset was processed to tag confirmed allergy, grade of severity, and culprit allergen identification associated with AC data and submitted to the data challenge.

Data were collected for 4271 patients, yielding a dataset with over 700,000 specific IgE data points. Sensitization was present in 3579 patients (84%). Allergy was confirmed in 2236 patients (53%) and excluded in 1076 patients, with the remaining 959 being missing outcome data (allergy diagnosis labels). The competition attracted 292 data scientists who submitted 3135 algorithms. The highest F scores ranged from 0.780 to 0.786. The database was subsequently made available as open source.

We present a nationwide open allergy database designed to enable the development of predictive algorithms. This scalable framework, integrating clinical data with machine learning techniques, paves the way for data-driven AC use and interpretation by allergists.