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Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning
Social forums offer a lot of new channels for collecting patients’ opinions to construct predictive models of adverse drug reactions (ADRs) for post-marketing surveillance. However, due to the characteristics of social posts, there are many challenges still to be solved when deriving such models, ma...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024774/ https://www.ncbi.nlm.nih.gov/pubmed/35455795 http://dx.doi.org/10.3390/healthcare10040618 |
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author | Huang, Jhih-Yuan Lee, Wei-Po Lee, King-Der |
author_facet | Huang, Jhih-Yuan Lee, Wei-Po Lee, King-Der |
author_sort | Huang, Jhih-Yuan |
collection | PubMed |
description | Social forums offer a lot of new channels for collecting patients’ opinions to construct predictive models of adverse drug reactions (ADRs) for post-marketing surveillance. However, due to the characteristics of social posts, there are many challenges still to be solved when deriving such models, mainly including problems caused by data sparseness, data features with a high-dimensionality, and term diversity in data. To tackle these crucial issues related to identifying ADRs from social posts, we perform data analytics from the perspectives of data balance, feature selection, and feature learning. Meanwhile, we design a comprehensive experimental analysis to investigate the performance of different data processing techniques and data modeling methods. Most importantly, we present a deep learning-based approach that adopts the BERT (Bidirectional Encoder Representations from Transformers) model with a new batch-wise adaptive strategy to enhance the predictive performance. A series of experiments have been conducted to evaluate the machine learning methods with both manual and automated feature engineering processes. The results prove that with their own advantages both types of methods are effective in ADR prediction. In contrast to the traditional machine learning methods, our feature learning approach can automatically achieve the required task to save the manual effort for the large number of experiments. |
format | Online Article Text |
id | pubmed-9024774 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90247742022-04-23 Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning Huang, Jhih-Yuan Lee, Wei-Po Lee, King-Der Healthcare (Basel) Article Social forums offer a lot of new channels for collecting patients’ opinions to construct predictive models of adverse drug reactions (ADRs) for post-marketing surveillance. However, due to the characteristics of social posts, there are many challenges still to be solved when deriving such models, mainly including problems caused by data sparseness, data features with a high-dimensionality, and term diversity in data. To tackle these crucial issues related to identifying ADRs from social posts, we perform data analytics from the perspectives of data balance, feature selection, and feature learning. Meanwhile, we design a comprehensive experimental analysis to investigate the performance of different data processing techniques and data modeling methods. Most importantly, we present a deep learning-based approach that adopts the BERT (Bidirectional Encoder Representations from Transformers) model with a new batch-wise adaptive strategy to enhance the predictive performance. A series of experiments have been conducted to evaluate the machine learning methods with both manual and automated feature engineering processes. The results prove that with their own advantages both types of methods are effective in ADR prediction. In contrast to the traditional machine learning methods, our feature learning approach can automatically achieve the required task to save the manual effort for the large number of experiments. MDPI 2022-03-25 /pmc/articles/PMC9024774/ /pubmed/35455795 http://dx.doi.org/10.3390/healthcare10040618 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Jhih-Yuan Lee, Wei-Po Lee, King-Der Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning |
title | Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning |
title_full | Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning |
title_fullStr | Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning |
title_full_unstemmed | Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning |
title_short | Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning |
title_sort | predicting adverse drug reactions from social media posts: data balance, feature selection and deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024774/ https://www.ncbi.nlm.nih.gov/pubmed/35455795 http://dx.doi.org/10.3390/healthcare10040618 |
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