Natural language processing for automated triage and prioritization of individual case safety reports for case-by-case assessment

Lieber, Thomas; Gosselt, Helen R.; Kools, Pelle C.; Kruijssen, Okko C.; Lierop, Stijn N. C. Van; Härmark, Linda; Hunsel, Florence van

doi:10.3389/fdsfr.2023.1120135

Cited by 2 publications

(1 citation statement)

References 38 publications

(39 reference statements)

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“…Lieber et al [8] train a bagging classifier of decision trees for the triage of Dutch ICSRs that require thorough clinical review. Their final model uses a set of 175 features including general information about patient and case, such as age, gender, weight, drug names, and seriousness information on the case as well as binary features on word occurrence for a selection of words deemed relevant by pharmacovigilance experts in the free-text fields and the length of text fields.…”

Section: Plos Digital Healthmentioning

confidence: 99%

BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance

Bergman,

Dürlich,

Arthurson

et al. 2023

PLOS Digit Health

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Post-marketing reports of suspected adverse drug reactions are important for establishing the safety profile of a medicinal product. However, a high influx of reports poses a challenge for regulatory authorities as a delay in identification of previously unknown adverse drug reactions can potentially be harmful to patients. In this study, we use natural language processing (NLP) to predict whether a report is of serious nature based solely on the free-text fields and adverse event terms in the report, potentially allowing reports mislabelled at time of reporting to be detected and prioritized for assessment. We consider four different NLP models at various levels of complexity, bootstrap their train-validation data split to eliminate random effects in the performance estimates and conduct prospective testing to avoid the risk of data leakage. Using a Swedish BERT based language model, continued language pre-training and final classification training, we achieve close to human-level performance in this task. Model architectures based on less complex technical foundation such as bag-of-words approaches and LSTM neural networks trained with random initiation of weights appear to perform less well, likely due to the lack of robustness that a base of general language training provides.

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Section: Plos Digital Healthmentioning

confidence: 99%