SpringerLink
Log in

Multiple features-based adverse drug reaction detection from social media using deep convolutional neural networks (DCNN)

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Adverse drug responses (ADRs) are unfavourable side effects of using a medication that result from the medication's pharmacological activity. Social media has gained popularity recently as a forum for individuals to discuss their health issues. As a result, it is becoming a common place to get natural language information on ADR. Drug-related problems and side effects are crucial concerns in medical diagnosis. There is a higher likelihood of identifying the adverse effects of medications used for a particular ailment based on comments made on social media. Most works are developed based on conventional text features and a decision-based classification process. This work proposed to perform adverse drug reaction detection from social media-related comments. The convolutional neural network-based techniques are proposed further to increase the accuracy at higher levels. Conventional techniques can be performed with particular kinds of drugs for only some local regions worldwide. The flexibility of the technique is poor in the recently proposed conventional techniques. To overcome this, we propose a deep convolutional neural network-based ADR structure with diverse training for better flexibility and accuracy. Multiple features such as sentiment, statistical, and medical keywords-related features are extracted to perform an accurate training process to obtain highly accurate results. Various sensitivity evaluation metrics, such as accuracy, sensitivity, and specificity values, are evaluated to validate the performance of the proposed method. This method is compared with another state of the art to analyze the performance comparison.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The data supporting this study's findings are openly available at https://huggingface.co/datasets/ade_corpus_v2.

References

  1. Wang J, Liang-Chih Yu, Zhang X (2022) Explainable detection of adverse drug reaction with imbalanced data distribution. PLoS Comput Biol 18(6):e1010144

    Article  Google Scholar 

  2. Alimova I, Tutubalina E (2019) Detecting adverse drug reactions from biomedical texts with neural networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pp. 415–421

  3. Hussain S, Afzal H, Saeed R, Iltaf N, Umair MY (2021) Pharmacovigilance with transformers: A framework to detect adverse drug reactions using BERT fine-tuned with FARM. Comput Math Methods Med 2021

  4. Li Z, Lin H, Zheng W (2020) An effective emotional expression and knowledge-enhanced method for detecting adverse drug reactions. IEEE Access 8:87083–87093

    Article  Google Scholar 

  5. Zhang Y, Cui S, Gao H (2020) Adverse drug reaction detection on social media with deep linguistic features. J Biomed Inform 106:103437

    Article  Google Scholar 

  6. Zhang M, Geng G (2019) Adverse drug event detection using a weakly supervised convolutional neural network and recurrent neural network model. Information 10(9):276

    Article  Google Scholar 

  7. El-allaly E-d, Sarrouti M, En-Nahnahi N, El Alaoui SO (2020) A LSTM-based method with attention mechanism for adverse drug reaction sentences detection. In: Advanced Intelligent Systems for Sustainable Development (AI2SD'2019) Volume 2-Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Health, Springer International Publishing, pp. 17–26

  8. Yousef RN, Mohammad ST, Omar N, Alshari EM (2020) Lexicon replacement method using word embedding technique for extracting adverse drug reaction. Int J Technol Manag Inf Syst 2(1):113–122

    Google Scholar 

  9. Zhang T, Lin H, Xu B, Ren Y, Yang Z, Wang J, Duan X (2020) Gated iterative capsule network for adverse drug reaction detection from social media. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp. 387–390

  10. Kayesh H, Saiful Islam Md, Wang J, Ohira R, Wang Z (2022) SCAN: A shared causal attention network for adverse drug reactions detection in tweets. Neurocomputing 479:60–74

    Article  Google Scholar 

  11. Gupta S, Pawar S, Ramrakhiyani N, KeshavPalshikar G, Varma V (2018) Semi-supervised recurrent neural network for adverse drug reaction mention extraction. BMC Bioinform 19:1–7

    Article  Google Scholar 

  12. Ding P, Zhou X, Zhang X, Wang J, Lei Z (2018) An attentive neural sequence labeling model for adverse drug reactions mentions extraction. IEEE Access 6:73305–73315

    Article  Google Scholar 

  13. Wu C, Wu F, Liu J, Wu S, Huang Y, **e X (2018) Detecting tweets mentioning drug name and adverse drug reaction with hierarchical tweet representation and multi-head self-attention. In: Proceedings of the 2018 EMNLP workshop SMM4H: the 3rd social media mining for health applications workshop & shared task, pp. 34–37

  14. Miranda DS (2018) Automated detection of adverse drug reactions in the biomedical literature using convolutional neural networks and biomedical word embeddings. ar**v preprint ar**v:1804.09148

  15. Sboev A, Rybka R, Gryaznov A, Moloshnikov I, Sboeva S, Rylkov G, Selivanov A (2022) Adverse Drug Reaction Concept Normalization in Russian-Language Reviews of Internet Users. Big Data Cogn Comput 6(4):145

    Article  Google Scholar 

  16. Rawat A, Wani MA, ElAffendi M, Imran AS, Kastrati Z, Daudpota SM (2022) Drug adverse event detection using text-based convolutional neural networks (TextCNN) technique. Electronics 11(20):3336

    Article  Google Scholar 

  17. Aroyehun ST, Gelbukh A (2018) Automatic identification of drugs and adverse drug reaction related tweets. In: Proceedings of the 2018 EMNLP workshop SMM4H: the 3rd social media mining for health applications workshop & shared task, pp. 54–55

  18. Alimova I, Tutubalina E (2018) Automated detection of adverse drug reactions from social media posts with machine learning. In: Analysis of Images, Social Networks and Texts: 6th International Conference, AIST 2017, Moscow, Russia, July 27–29, 2017, Revised Selected Papers 6, Springer International Publishing, pp. 3–15

  19. Lambay MA, Pakkir Mohideen S, Abdur Rahman BS (2022) AIE-DRP: Framework with Machine Learning and Deep Learning Models for Adverse Drug Reaction Prediction in Healthcare Use Case. In: 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, vol. 1, pp. 1330–1336

  20. Úbeda PL, Díaz-Galiano MC, Martín-Valdivia MT, Alfonso Urena Lopez L (2019) Using machine learning and deep learning methods to find mentions of adverse drug reactions in social media. In: Proceedings of the fourth social media mining for health applications (# SMM4H) workshop & shared task, pp. 102–106

  21. Kayesh H, Saiful Islam Md, Wang J, Ohira R, Wang Z (2022) SCAN: A shared causal attention network for adverse drug reactions detection in tweets. Neurocomputing 479:60–74

    Article  Google Scholar 

  22. Zhang T, Lin H, Ren Y, Yang L, Bo Xu, Yang Z, Wang J, Zhang Y (2019) Adverse drug reaction detection via a multihop self-attention mechanism. BMC Bioinform 20(1):1–11

    Article  Google Scholar 

  23. Zhang T, Lin H, Bo Xu, Yang L, Wang J, Duan X (2021) Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions. J Biomed Inform 123:103896

    Article  Google Scholar 

  24. Shen C, Lin H, Guo K, Kan Xu, Yang Z, Wang J (2019) Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks. Neural Comput Appl 31:4799–4808

    Article  Google Scholar 

Download references

Funding

There are no funding sources for this work.

Author information

Authors and Affiliations

  1. Department of CS & AI, SR University, Warangal, Telangana, India

    S. Spandana & R. Vijaya Prakash

Authors
  1. S. Spandana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Vijaya Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Spandana.

Ethics declarations

Conflict of interest

The corresponding author declares that there are no competing interests on behalf of the other writers.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Spandana, S., Prakash, R.V. Multiple features-based adverse drug reaction detection from social media using deep convolutional neural networks (DCNN). Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18144-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18144-9

Keywords

Search

Navigation