An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

Yu, Deahan; Vydiswaran, V. G. Vinod

doi:10.2196/38140

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…Additionally, automated identification of adverse drug events 116 , 117 or post-operative complications 118 provides the opportunity to identify otherwise unrecognized adverse events or for identification of novel drug targets. 119 When automizing such screening, social media can also be utilized to track healthcare status 120 , 121 or identify adverse drug events, 122 thereby broadening insights from clinical trials to the real world.…”

Section: Clinical Applications Of Large Language Models In Cardiologymentioning

confidence: 99%

Artificial intelligence: revolutionizing cardiology with large language models

Boonstra,

Weissenbacher,

Moore

et al. 2024

European Heart Journal

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Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.

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Section: Clinical Applications Of Large Language Models In Cardiologymentioning

confidence: 99%

Artificial intelligence: revolutionizing cardiology with large language models

Boonstra,

Weissenbacher,

Moore

et al. 2024

European Heart Journal

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“…For example, Guo et al focused on identifying occurrences of COVID-19 and related symptoms as conveyed by users on Reddit ( 9 ). Simultaneously, Yu and Vydiswaran utilized natural language processing (NLP) algorithms to identify and classify Twitter posts related to drug adverse events ( 10 ). This exemplifies the application of advanced computational methods in drug safety surveillance.…”

Section: Introductionmentioning

confidence: 99%

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices

Dong,

Guo,

Liu

et al. 2024

Front. Public Health

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IntroductionSocial media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain.MethodRecognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection.ResultThe hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively.DiscussionThis study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

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“…The applications of deep learning for natural language processing have expanded dramatically in recent years [ 25 ]. Since the development of a high-performance deep learning model in 2018 [ 26 ], attempts to apply cutting-edge deep learning models to various kinds of patient-generated text data for the evaluation of safety events or the analysis of unscalable subjective information from patients have been accelerating [ 27 - 31 ]. Most studies have been conducted to use patients’ narrative data for pharmacovigilance [ 27 , 32 - 35 ], while few have been aimed at improvement of real-time safety monitoring for individual patients.…”

Section: Introductionmentioning

confidence: 99%

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Nishioka,

Watabe,

Yanagisawa

et al. 2024

J Med Internet Res

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Background Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. Objective This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed. Methods Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. Results From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives. Conclusions Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

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An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis

Cited by 11 publications

References 25 publications

Artificial intelligence: revolutionizing cardiology with large language models

Artificial intelligence: revolutionizing cardiology with large language models

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

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