Bert Vallee—A 20th Century Adventure(r) in Zincology

Jacob, Claus; Abdin, Ahmad Yaman; Köhler, Frederieke; Maret, Wolfgang

doi:10.3390/ijms222413393

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Its proficiency in capturing contextual information allows it to comprehend the intricacies of language exceptionally well, leading to superior performance across various NLP tasks. There are several variants of the BERT model, each tailored for specific purposes or devised to address particular limitations of the original model ( 43 – 47 ). The selection of the appropriate BERT-based model depends on various factors, including the nature of the task, dataset size, computational resources, and language requirements.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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Moonlighting enzymes: when cellular context defines specificity

Gupta

Uversky

2023

Cell. Mol. Life Sci.

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Hemoperfusion in the intensive care unit

et al. 2022

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Multiple organ failure following a septic event derives from immune dysregulation. Many of the mediators of this process are humoral factors (cytokines), which could theoretically be cleared by direct adsorption through a process called hemoperfusion. Hemoperfusion through devices, which bind specific molecules like endotoxin or theoretically provide non-specific adsorption of pro-inflammatory mediators has been attempted and studied for several decades with variable results. More recently, technological evolution has led to the increasing application of adsorption due to more biocompatible and possibly more efficient biomaterials. As a result, new indications are developing in this field, and novel tools are available for clinical use. This narrative review will describe current knowledge regarding technical concepts, safety, and clinical results of hemoperfusion. Finally, it will focus on the most recent literature regarding adsorption applied in critically ill patients and their indications, including recent randomized controlled trials and future areas of investigation.

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Bert Vallee—A 20th Century Adventure(r) in Zincology

Cited by 3 publications

References 26 publications

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

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

Moonlighting enzymes: when cellular context defines specificity

Hemoperfusion in the intensive care unit

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