Computational Approaches to Penicillin Allergy De-labelling: A Clinical Review

Elkhalifa, Shuayb and Devey, Jack and Al-Asali, Abdulrahaman and Elbashir, Haggar and Ćalasan, Maja Bulatović and Pleijhuis, Rick G. and Terreehorst, Ingrid and Salvo, Fulvio and Shafiq, Irfan and Isse, Said and Abuzakouk, Mohamed and Gaber, Mohamed Medhat and Bhana, Rehan (2026) Computational Approaches to Penicillin Allergy De-labelling: A Clinical Review. Clinical Reviews in Allergy & Immunology, 69 (1). ISSN 1559-0267

Preview

Text s12016-026-09203-0.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB)

Abstract

Penicillin allergy is recorded in roughly one in ten hospital patients, yet fewer than one in ten of those records reflects a real, clinically significant allergy. The mismatch matters: patients with a penicillin label receive more second-line and broader-spectrum antibiotics, with measurable downstream costs in infection, surgical site morbidity, length of stay, and antimicrobial resistance. Specialist-led skin testing and graded oral challenge remain the diagnostic standard, but the global allergy workforce cannot test the population that needs testing. Validated decision rules such as PEN-FAST, structured non-allergist pathways, and direct oral challenge in low-risk patients have closed part of that gap, but uptake is still limited by inconsistent free-text documentation, variable clinician confidence, and weak integration with electronic health records. This review summarises how computational methods are being added to that pathway. Supervised machine-learning models can mine electronic health records to identify candidates for review. Natural language processing can recover the timing, character, and severity of a reaction from clinic letters that the structured allergy field omitted. Predictive models that combine clinical, laboratory, and immunological inputs are starting to outperform rule-based scores on beta-lactam risk. Decision-support systems are moving past simple drug-name matching toward context-aware alerts. Image classifiers and large language models are opening a route to rapid, photograph-and-history triage. We discuss what is needed for any of this to be safe in routine care: representative training data, transparent model behaviour, regulatory alignment, and prospective evaluation against the outcomes the antimicrobial stewardship community already measures.

Item Type:	Article
Identification Number:	10.1007/s12016-026-09203-0
Dates:	Date Event 12 August 2026 Accepted 22 September 2026 Published Online
Uncontrolled Keywords:	Penicillin allergy, Drug allergy, De-labelling, Machine learning, Natural language processing, Clinical decision support, Antimicrobial stewardship
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	06 Nov 2026 12:13
Last Modified:	06 Nov 2026 12:13
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17299

Actions (login required)

View Item