A Highly Accurate Adverse Drug Reactions (ADR) Detection from Medical Forum Comments Using Long Short-Term Memory Networks
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Abstract
Adverse drug reactions (ADRs) are only one example of the kind of helpful medical information that is frequently accessible on social media platforms for healthcare, where people can share their own experiences with treatments on desktop computers and mobile devices. A growing number of researchers are interested in gathering this valuable medical data from social media. This research explores the effects of three aspects on the recognition of ADR mentions in social media for the medical field and proposes a deep neural network of long short-term memory (LSTM) neural networks to do so. The texts from the dataset are pre-processed, and characteristics such as the semantic feature, text statics, and American Standard Code For Information Interchange (ASCII) array are extracted. Further, the features are converted into LSTM networks to perform the testing operation using the above features. This work was evaluated using two datasets, CODEC and ADR Corpus. Via extensive experiments, this work achieved 99.79 accuracies, 98.37 sensitivity, 97.63 specificities, 99.72 precision, and 98.39 recall 97.62 F1-score for the CODEC dataset. 98.16 for accuracy, 99.19 for sensitivity, 98.49 for specificity, 99.49 for precision, 96.72 for recall, and 93.16 for F1-score for ADE corpus, respectively.
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23- Peng Ding, Xiaobing Zhou, Xuejie Zhang, Jin Wang, and Zhenfeng Lei, "An attentive neural sequence labeling model for adverse drug reactions mentions extraction." Ieee Access, Vol. 6pp. 73305-15, (2018).
24- Daniel S Budnitz, Nadine Shehab, Scott R Kegler, and Chesley L Richards, "Medication use leading to emergency department visits for adverse drug events in older adults." Annals of internal medicine, Vol. 147 (No. 11), pp. 755-65, (2007).
25- Robert P Schumaker, Michael A Veronin, Trevor Rohm, Matthew Boyett, and Rohit R Dixit, "A data driven approach to profile potential SARS-CoV-2 drug interactions using TylerADE." Journal of International Technology and Information Management, Vol. 30 (No. 3), pp. 108-42, (2021).
26- Eva-Lisa Meldau, "Deep neural networks for inverse de-identification of medical case narratives in reports of suspected adverse drug reactions." ed, (2018).
27- Nitin Ramrakhiyani and Girish Keshav Palshikar, "Multi-task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets." in Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings, (2018), Vol. 10772: Springer, p. 59.
2- Anne Cocos, Alexander G Fiks, and Aaron J Masino, "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts." Journal of the American Medical Informatics Association, Vol. 24 (No. 4), pp. 813-21, (2017).
3- IS Alimova and EV Tutubalina, "Entity-level classification of adverse drug reaction: A comparative analysis of neural network models." Programming and Computer Software, Vol. 45pp. 439-47, (2019).
4- Claudia Giardina et al., "Adverse drug reactions in hospitalized patients: results of the FORWARD (Facilitation of Reporting in Hospital Ward) study." Frontiers in pharmacology, Vol. 9p. 350, (2018).
5- Jonathan A Galli, Aloknath Pandya, Michelle Vega‐Olivo, Chandra Dass, Huaqing Zhao, and Gerard J Criner, "Pirfenidone and nintedanib for pulmonary fibrosis in clinical practice: tolerability and adverse drug reactions." Respirology, Vol. 22 (No. 6), pp. 1171-78, (2017).
6- Mehdi Gheisari et al., "Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey." CAAI Transactions on Intelligence Technology, (2023).
7- Mustafa Ghaderzadeh, Mehrad Aria, Azamossadat Hosseini, Farkhondeh Asadi, Davood Bashash, and Hassan Abolghasemi, "A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images." International Journal of Intelligent Systems, Vol. 37 (No. 8), pp. 5113-33, (2022).
8- Mustafa Ghaderzadeh, Farkhondeh Asadi, Ramezan Jafari, Davood Bashash, Hassan Abolghasemi, and Mehrad Aria, "Deep convolutional neural network–based computer-aided detection system for COVID-19 using multiple lung scans: design and implementation study." Journal of Medical Internet Research, Vol. 23 (No. 4), p. e27468, (2021).
9- Elena Tutubalina and Sergey Nikolenko, "Combination of deep recurrent neural networks and conditional random fields for extracting adverse drug reactions from user reviews." Journal of healthcare engineering, Vol. 2017(2017).
10- Mert Tiftikci, Arzucan Özgür, Yongqun He, and Junguk Hur, "Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels." BMC bioinformatics, Vol. 20 (No. 21), pp. 1-9, (2019).
11- Kathy Lee et al., "Adverse drug event detection in tweets with semi-supervised convolutional neural networks." in Proceedings of the 26th international conference on world wide web, (2017), pp. 705-14.
12- Chuhan Wu, Fangzhao Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang, and Xing Xie, "MSA: Jointly detecting drug name and adverse drug reaction mentioning tweets with multi-head self-attention." in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, (2019), pp. 33-41.
13- Liliya Akhtyamova, Mikhail Alexandrov, and John Cardiff, "Adverse drug extraction in twitter data using convolutional neural network." in 2017 28th International Workshop on Database and Expert Systems Applications (DEXA), (2017): IEEE, pp. 88-92.
14- Sunil Kumar Sahu and Ashish Anand, "Drug-drug interaction extraction from biomedical texts using long short-term memory network." Journal of biomedical informatics, Vol. 86pp. 15-24, (2018).
15- Bharath Dandala, Venkata Joopudi, and Murthy Devarakonda, "Adverse drug events detection in clinical notes by jointly modeling entities and relations using neural networks." Drug safety, Vol. 42pp. 135-46, (2019).
16- Jing Liu, Songzheng Zhao, and Xiaodi Zhang, "An ensemble method for extracting adverse drug events from social media." Artificial intelligence in medicine, Vol. 70pp. 62-76, (2016).
17- Hong-Jie Dai, Musa Touray, Jitendra Jonnagaddala, and Shabbir Syed-Abdul, "Feature engineering for recognizing adverse drug reactions from twitter posts." Information, Vol. 7 (No. 2), p. 27, (2016).
18- Zahra Rezaei, Hossein Ebrahimpour-Komleh, Behnaz Eslami, Ramyar Chavoshinejad, and Mehdi Totonchi, "Adverse drug reaction detection in social media by deep learning methods." Cell Journal (Yakhteh), Vol. 22 (No. 3), p. 319, (2020).
19- Sarthak Jain, Xun Peng, and Byron C Wallace, "Detecting Twitter posts with Adverse Drug Reactions using Convolutional Neural Networks." in SMM4H@ AMIA, (2017), pp. 72-75.
20- KM Deepa Lakshmi and A Prakash, "Evaluating the Adverse Drug Reactions Learning Framework Using Novel Text Mining." International Journal of Applied Engineering Research, Vol. 13 (No. 21), pp. 14983-96, (2018).
21- Sarvnaz Karimi, Chen Wang, Alejandro Metke-Jimenez, Raj Gaire, and Cecile Paris, "Text and data mining techniques in adverse drug reaction detection." ACM Computing Surveys (CSUR), Vol. 47 (No. 4), pp. 1-39, (2015).
22- Shashank Gupta, Manish Gupta, Vasudeva Varma, Sachin Pawar, Nitin Ramrakhiyani, and Girish Keshav Palshikar, "Multi-task learning for extraction of adverse drug reaction mentions from tweets." in European Conference on Information Retrieval, (2018): Springer, pp. 59-71.
23- Peng Ding, Xiaobing Zhou, Xuejie Zhang, Jin Wang, and Zhenfeng Lei, "An attentive neural sequence labeling model for adverse drug reactions mentions extraction." Ieee Access, Vol. 6pp. 73305-15, (2018).
24- Daniel S Budnitz, Nadine Shehab, Scott R Kegler, and Chesley L Richards, "Medication use leading to emergency department visits for adverse drug events in older adults." Annals of internal medicine, Vol. 147 (No. 11), pp. 755-65, (2007).
25- Robert P Schumaker, Michael A Veronin, Trevor Rohm, Matthew Boyett, and Rohit R Dixit, "A data driven approach to profile potential SARS-CoV-2 drug interactions using TylerADE." Journal of International Technology and Information Management, Vol. 30 (No. 3), pp. 108-42, (2021).
26- Eva-Lisa Meldau, "Deep neural networks for inverse de-identification of medical case narratives in reports of suspected adverse drug reactions." ed, (2018).
27- Nitin Ramrakhiyani and Girish Keshav Palshikar, "Multi-task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets." in Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings, (2018), Vol. 10772: Springer, p. 59.
| Files | ||
| Issue | Vol 11 No 4 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i4.16508 | |
| Keywords | ||
| Adverse Drug Reactions Medical Information Long Short-Term Memory(LSTM) American Standard Code For Information Interchange Sensitivity Evaluation. | ||
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Basagodu Veeresh A, Guralamata Krishnegowda R, Salekoppalu Venkataramu S. A Highly Accurate Adverse Drug Reactions (ADR) Detection from Medical Forum Comments Using Long Short-Term Memory Networks. Frontiers Biomed Technol. 2023;11(4):597-606.
