2021
DOI: 10.1016/j.ijmedinf.2021.104611
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Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing

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Cited by 19 publications
(34 citation statements)
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“…Systematic penicillin allergy delabeling may assist with the accuracy of these EMRs. Machine learning may be able to assist with this task [8]. Penicillin allergy delabeling may result in some individuals having their AR label changed from "allergy" to "intolerance".…”
Section: Discussionmentioning
confidence: 99%
“…Systematic penicillin allergy delabeling may assist with the accuracy of these EMRs. Machine learning may be able to assist with this task [8]. Penicillin allergy delabeling may result in some individuals having their AR label changed from "allergy" to "intolerance".…”
Section: Discussionmentioning
confidence: 99%
“…Having a penicillin allergy label is associated with higher healthcare resource utilization, emphasizing the need for accurate diagnosis and labeling [ 59 ]. ML has been used to risk stratify, and potentially de-label penicillin allergy with medical record analysis playing a key role in addressing drug allergy at a population level [ 60 , 61 , 62 •, 63 , 64 ].…”
Section: Key Clinical Applications Of Ai In Allergymentioning
confidence: 99%
“…An ANN trained on clinical data from patients with and without confirmed beta-lactam allergy at a single center was able to prospectively predict beta-lactam allergy with an AUC of 0.939 [ 61 ]. Another ANN trained on EHR data from patients with labeled penicillin allergy was able to distinguish between true allergy and intolerance with an AUC of 0.994 when compared with expert criteria and manual chart review [ 60 ].…”
Section: Key Clinical Applications Of Ai In Allergymentioning
confidence: 99%
“…Nonetheless, it is unlikely that a severe reaction (e.g., Stevens Johnson syndrome/toxic epidermal necrolysis, drug hypersensitivity syndrome or anaphylaxis) in the index history is missed unless there the patient has no recollection of hospitalization for the serious drug reaction, especially in elderly patients with cognitive impairment, or where no immediate family members witnessed or are able to recall the event either. Machine learning using datasets derived from electronic medical records and other digital assessment tools may in future help facilitate classification of index adverse drug reactions and risk assessment ( 21 , 22 ). Structured and validated clinical decision tools or guidance, such as PEN-FAST, are straightforward and have also been demonstrated to aid with risk stratification ( 23 ).…”
Section: Nomenclature and Definitionsmentioning
confidence: 99%