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Explainable detection of adverse drug reaction with imbalanced data distribution

Analysis of health-related texts can be used to detect adverse drug reactions (ADR). The greatest challenge for ADR detection lies in imbalanced data distributions where words related to ADR symptoms are often minority classes. As a result, trained models tend to converge to a point that strongly bi...

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Detalles Bibliográficos
Autores principales: Wang, Jin, Yu, Liang-Chih, Zhang, Xuejie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239481/
https://www.ncbi.nlm.nih.gov/pubmed/35704662
http://dx.doi.org/10.1371/journal.pcbi.1010144
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author Wang, Jin
Yu, Liang-Chih
Zhang, Xuejie
author_facet Wang, Jin
Yu, Liang-Chih
Zhang, Xuejie
author_sort Wang, Jin
collection PubMed
description Analysis of health-related texts can be used to detect adverse drug reactions (ADR). The greatest challenge for ADR detection lies in imbalanced data distributions where words related to ADR symptoms are often minority classes. As a result, trained models tend to converge to a point that strongly biases towards the majority class and then ignores the minority class. Since the most used cross-entropy criteria is an approximation to accuracy, the model focuses more readily on the majority class to achieve high accuracy. To address this issue, existing methods apply either oversampling or down-sampling strategies to balance the data distribution and exploit the most difficult samples of the minority class. However, increasing or reducing the number of individual tokens alone in sequence labeling tasks will result in the loss of the syntactic relations of the sentence. This paper proposes a weighted variant of conditional random field (CRF) for data-imbalanced sequence labeling tasks. Such a weighting strategy can alleviate data distribution imbalances between majority and minority classes. Instead of using softmax in the output layer, the CRF can capture the relationship of labels between tokens. The locally interpretable model-agnostic explanations (LIME) algorithm was applied to investigate performance differences between models with and without the weighted loss function. Experimental results on two different ADR tasks show that the proposed model outperforms previously proposed sequence labeling methods.
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spelling pubmed-92394812022-06-29 Explainable detection of adverse drug reaction with imbalanced data distribution Wang, Jin Yu, Liang-Chih Zhang, Xuejie PLoS Comput Biol Research Article Analysis of health-related texts can be used to detect adverse drug reactions (ADR). The greatest challenge for ADR detection lies in imbalanced data distributions where words related to ADR symptoms are often minority classes. As a result, trained models tend to converge to a point that strongly biases towards the majority class and then ignores the minority class. Since the most used cross-entropy criteria is an approximation to accuracy, the model focuses more readily on the majority class to achieve high accuracy. To address this issue, existing methods apply either oversampling or down-sampling strategies to balance the data distribution and exploit the most difficult samples of the minority class. However, increasing or reducing the number of individual tokens alone in sequence labeling tasks will result in the loss of the syntactic relations of the sentence. This paper proposes a weighted variant of conditional random field (CRF) for data-imbalanced sequence labeling tasks. Such a weighting strategy can alleviate data distribution imbalances between majority and minority classes. Instead of using softmax in the output layer, the CRF can capture the relationship of labels between tokens. The locally interpretable model-agnostic explanations (LIME) algorithm was applied to investigate performance differences between models with and without the weighted loss function. Experimental results on two different ADR tasks show that the proposed model outperforms previously proposed sequence labeling methods. Public Library of Science 2022-06-15 /pmc/articles/PMC9239481/ /pubmed/35704662 http://dx.doi.org/10.1371/journal.pcbi.1010144 Text en © 2022 Wang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Jin
Yu, Liang-Chih
Zhang, Xuejie
Explainable detection of adverse drug reaction with imbalanced data distribution
title Explainable detection of adverse drug reaction with imbalanced data distribution
title_full Explainable detection of adverse drug reaction with imbalanced data distribution
title_fullStr Explainable detection of adverse drug reaction with imbalanced data distribution
title_full_unstemmed Explainable detection of adverse drug reaction with imbalanced data distribution
title_short Explainable detection of adverse drug reaction with imbalanced data distribution
title_sort explainable detection of adverse drug reaction with imbalanced data distribution
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239481/
https://www.ncbi.nlm.nih.gov/pubmed/35704662
http://dx.doi.org/10.1371/journal.pcbi.1010144
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