Extensive evaluation of transformer-based architectures for adverse drug events extraction

Scaboro, Simone; Portelli, Beatrice; Chersoni, Emmanuele; Santus, Enrico; Serra, Giuseppe

doi:10.1016/j.knosys.2023.110675

Cited by 6 publications

(3 citation statements)

References 59 publications

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“…Graph-based embedding is a novel technique employed in this study that has not been used to predict ADE onset times before. In addition, the authors of [17] discussed the importance of extracting ADEs from informal text data, such as tweets and forum posts, for digital pharmacovigilance. They emphasized the growing use of large pretrained language models, such as BERT, for this task, but acknowledged that it is unclear which transformer-based models are the most effective.…”

Section: Related Workmentioning

confidence: 99%

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

Aldughayfiq,

Allahem,

Mostafa

et al. 2024

Applied Sciences

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In this study, we introduce a novel combination of layer-static-weighted attention and ascending feature selection techniques to predict the seriousness level of adverse drug events using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized natural language processing (NLP) to analyze the terms in the active substance field, in addition to considering demographic and event information such as patient sex, healthcare provider qualification, and drug characterization. Our ascending feature selection method, which progressively incorporates additional features based on their importance, demonstrated continuous enhancements in prediction performance. Simultaneously, we employed a layer-static-weighted attention technique, which dynamically adjusts the model’s focus between natural language processing (NLP) and demographic features. This technique achieved its best performance at a balanced weight of 50%, yielding an average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included, indicating a compelling advantage to include a larger volume of meaningful features. By integrating these methodologies, we constructed a robust model capable of effectively predicting seriousness levels, offering significant potential for improving pharmacovigilance and enhancing drug safety monitoring. The results underscore the value of NLP and demographic data in predicting drug event seriousness and demonstrate the effectiveness of our combined techniques. We encourage further research to refine these methods and evaluate their application to other clinical datasets.

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Section: Related Workmentioning

confidence: 99%

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

Aldughayfiq,

Allahem,

Mostafa

et al. 2024

Applied Sciences

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show abstract

“…An extract-then-abstract based method to generate disaster-news headlines using a DNN extractor followed by a transformer abstractor [244]. Extensive evaluation of transformer-based architectures for adverse drug events extraction [291]. Dialog summarization for software collaborative platform via tuning pre-trained models [250].…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

“…Paraphrasing techniques are a linchpin in recognizing or generating expressions and sentences with akin meanings. With a keen focus on generating coherent and semantically akin paraphrases, this model, powered by a finelytuned Text-To-Text Transfer Transformer (T5) [244], [248], [250], [260], [165], [223], [290], [291], [305] XLNet Personality is a complex mix of traits that define how we think, act, and feel, and it's studied in various fields like healthcare and social media. smart computer models like XLNet to predict personality traits from text, making it more accurate and helpful.…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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EADR: an ensemble learning method for detecting adverse drug reactions from twitter

Keyvanpour,

Pourebrahim,

Mehrmolaei

2024

Soc. Netw. Anal. Min.

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Extensive evaluation of transformer-based architectures for adverse drug events extraction

Cited by 6 publications

References 59 publications

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

EADR: an ensemble learning method for detecting adverse drug reactions from twitter

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