Add like
Add dislike
Add to saved papers

Algorithmic identification of treatment-emergent adverse events from clinical notes using large language models: a pilot study in inflammatory bowel disease.

medRxiv 2023 September 9
BACKGROUND AND AIMS: Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLM) like BERT have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event detection.

METHODS: We adapted a new clinical LLM, UCSF BERT, to identify serious adverse events (SAEs) occurring after treatment with a non-steroid immunosuppressant for inflammatory bowel disease (IBD). We compared this model to other language models that have previously been applied to AE detection.

RESULTS: We annotated 928 outpatient IBD notes corresponding to 928 individual IBD patients for all SAE-associated hospitalizations occurring after treatment with a non-steroid immunosuppressant. These notes contained 703 SAEs in total, the most common of which was failure of intended efficacy. Out of 8 candidate models, UCSF BERT achieved the highest numerical performance on identifying drug-SAE pairs from this corpus (accuracy 88-92%, macro F1 61-68%), with 5-10% greater accuracy than previously published models. UCSF BERT was significantly superior at identifying hospitalization events emergent to medication use (p < 0.01).

CONCLUSIONS: LLMs like UCSF BERT achieve numerically superior accuracy on the challenging task of SAE detection from clinical notes compared to prior methods. Future work is needed to adapt this methodology to improve model performance and evaluation using multi-center data and newer architectures like GPT. Our findings support the potential value of using large language models to enhance pharmacovigilance.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Group 7SearchHeart failure treatmentPapersTopicsCollectionsEffects of Sodium-Glucose Cotransporter 2 Inhibitors for the Treatment of Patients With Heart Failure Importance: Only 1 class of glucose-lowering agents-sodium-glucose cotransporter 2 (SGLT2) inhibitors-has been reported to decrease the risk of cardiovascular events primarily by reducingSeptember 1, 2017: JAMA CardiologyAssociations of albuminuria in patients with chronic heart failure: findings in the ALiskiren Observation of heart Failure Treatment study.CONCLUSIONS: Increased UACR is common in patients with heart failure, including non-diabetics. Urinary albumin creatininineJul, 2011: European Journal of Heart FailureRandomized Controlled TrialEffects of Liraglutide on Clinical Stability Among Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial.Review

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

Read by QxMD is copyright © 2021 QxMD Software Inc. All rights reserved. By using this service, you agree to our terms of use and privacy policy.

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app