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Deep learning for pollen allergy surveillance from twitter in Australia
BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternativ...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839169/ https://www.ncbi.nlm.nih.gov/pubmed/31699071 http://dx.doi.org/10.1186/s12911-019-0921-x |
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author | Rong, Jia Michalska, Sandra Subramani, Sudha Du, Jiahua Wang, Hua |
author_facet | Rong, Jia Michalska, Sandra Subramani, Sudha Du, Jiahua Wang, Hua |
author_sort | Rong, Jia |
collection | PubMed |
description | BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. METHODS: The data was extracted from Twitter based on pre-defined keywords (i.e. ’hayfever’ OR ’hay fever’) throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed. RESULTS: The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions). CONCLUSIONS: The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of ’black-box’ approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain. |
format | Online Article Text |
id | pubmed-6839169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68391692019-11-12 Deep learning for pollen allergy surveillance from twitter in Australia Rong, Jia Michalska, Sandra Subramani, Sudha Du, Jiahua Wang, Hua BMC Med Inform Decis Mak Research Article BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. METHODS: The data was extracted from Twitter based on pre-defined keywords (i.e. ’hayfever’ OR ’hay fever’) throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed. RESULTS: The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions). CONCLUSIONS: The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of ’black-box’ approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain. BioMed Central 2019-11-08 /pmc/articles/PMC6839169/ /pubmed/31699071 http://dx.doi.org/10.1186/s12911-019-0921-x Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Rong, Jia Michalska, Sandra Subramani, Sudha Du, Jiahua Wang, Hua Deep learning for pollen allergy surveillance from twitter in Australia |
title | Deep learning for pollen allergy surveillance from twitter in Australia |
title_full | Deep learning for pollen allergy surveillance from twitter in Australia |
title_fullStr | Deep learning for pollen allergy surveillance from twitter in Australia |
title_full_unstemmed | Deep learning for pollen allergy surveillance from twitter in Australia |
title_short | Deep learning for pollen allergy surveillance from twitter in Australia |
title_sort | deep learning for pollen allergy surveillance from twitter in australia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839169/ https://www.ncbi.nlm.nih.gov/pubmed/31699071 http://dx.doi.org/10.1186/s12911-019-0921-x |
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