Adverse drug event detection using reason assignments in FDA drug labels

Sutphin, Corey; Lee, Kahyun; Jimeno-Yepes, Antonio; Uzuner, Özlem; McInnes, Bridget T.

doi:10.1016/j.jbi.2020.103552

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…Studies have shown that information overload can increase mental effort and be fatal when it hinders understanding medical and health-related information [30]. The usage of the BERT model in text summarization reduced the length of the document while preserving the essence of the document, as reported in earlier studies [23,26,28]. Additionally, our results showed that, despite the simplification of the auto-generated summarized text to save the readers time and effort, the knowledge gained by those who read the summarized text was comparable to those who read the original text.…”

Section: Empirical Implicationsmentioning

confidence: 79%

“…The system maps health-related entities mentioned in social media free text such as symptoms, drugs, or adverse drug reactions into formal medical concepts. The BERT model was also used by [28] to extract Adverse Drug Events (ADEs) and their side effects from Food and Drug Administration (FDA) drug labels. Datta et al [12] used the BERT model on a radiology report named Rad-SpRL to extract special information about patients, whereas, in [26], the model was used to extract comprehensive information covering clinical concepts and relations from a clinical report dataset written in the Chinese language.…”

Section: Deep Learning In the Medical Domainmentioning

confidence: 99%

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Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study

Bitar

Babour

Nafa

et al. 2022

IJERPH

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Despite the availability of online educational resources about human papillomavirus (HPV), many women around the world may be prevented from obtaining the necessary knowledge about HPV. One way to mitigate the lack of HPV knowledge is the use of auto-generated text summarization tools. This study compares the level of HPV knowledge between women who read an auto-generated summary of HPV made using the BERT deep learning model and women who read a long-form text of HPV. We randomly assigned 386 women to two conditions: half read an auto-generated summary text about HPV (n = 193) and half read an original text about HPV (n = 193). We administrated measures of HPV knowledge that consisted of 29 questions. As a result, women who read the original text were more likely to correctly answer two questions on the general HPV knowledge subscale than women who read the summarized text. For the HPV testing knowledge subscale, there was a statistically significant difference in favor of women who read the original text for only one question. The final subscale, HPV vaccination knowledge questions, did not significantly differ across groups. Using BERT for text summarization has shown promising effectiveness in increasing women’s knowledge and awareness about HPV while saving their time.

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