|
|||||
|
|
Machine learning models automate classification of penicillin adverse drug reaction labels |
|
| Authors: | Joshua M. Inglis Stephen Bacchi Alexander Troelnikov William Smith Sepehr Shakib |
| Affiliation: | 1. Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, Australia;2. Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, Australia Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia;3. Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia Clinical Immunology & Allergy, Royal Adelaide Hospital, Adelaide, South Australia, Australia Flinders University, Adelaide, South Australia, Australia;4. Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia Clinical Immunology & Allergy, Royal Adelaide Hospital, Adelaide, South Australia, Australia;5. Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, South Australia, Australia Discipline of Pharmacology, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia |
| Abstract: | There is a growing interest in the appropriate evaluation of penicillin adverse drug reaction (ADR) labels. We have developed machine learning models for classifying penicillin ADR labels using free-text reaction descriptions, and here report external and practical validation. The models performed comparably with expert criteria for the categorisation of allergy or intolerance and identification of high-risk allergies. These models have practical applications in detecting individuals suitable for penicillin ADR evaluation. Implementation studies are required. |
| Keywords: | penicillin adverse drug reaction electronic health records deep learning natural language processing |