Skip to main content
SpringerLink
Log in

SR-CoMbEr: Heterogeneous Network Embedding Using Community Multi-view Enhanced Graph Convolutional Network for Automating Systematic Reviews

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13980))

Included in the following conference series:

  • 1384 Accesses

Abstract

Systematic reviews (SRs) are a crucial component of evidence-based clinical practice. Unfortunately, SRs are labor-intensive and unscalable with the exponential growth in literature. Automating evidence synthesis using machine learning models has been proposed but solely focuses on the text and ignores additional features like citation information. Recent work demonstrated that citation embeddings can outperform the text itself, suggesting that better network representation may expedite SRs. Yet, how to utilize the rich information in heterogeneous information networks (HIN) for network embeddings is understudied. Existing HIN models fail to produce a high-quality embedding compared to simply running state-of-the-art homogeneous network models. To address existing HIN model limitations, we propose SR-CoMbEr, a community-based multi-view graph convolutional network for learning better embeddings for evidence synthesis. Our model automatically discovers article communities to learn robust embeddings that simultaneously encapsulate the rich semantics in HINs. We demonstrate the effectiveness of our model to automate 15 SRs.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.ncbi.nlm.nih.gov/books/NBK25501/.

  2. 2.

    https://github.com/ewhlee/SR-CoMbEr.

References

  1. Bannach-Brown, A., et al.: Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error. Systematic Rev. 8(1), 23 (2019)

    Google Scholar 

  2. Bastian, H., Glasziou, P., Chalmers, I.: Seventy-five trials and eleven systematic reviews a day: How will we ever keep up? PLOS Med. 7(9), e1000326 (2010)

    Google Scholar 

  3. Borah, R., Brown, A.W., Capers, P.L., Kaiser, K.A.: Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the prospero registry. BMJ open 7(2), e012545 (2017)

    Google Scholar 

  4. Cadena, C., Dick, A.R., Reid, I.D.: Multi-modal auto-encoders as joint estimators for robotics scene understanding. In: Robotics: Science and Systems (2016)

    Google Scholar 

  5. Chandler, J., Churchill, R., Higgins, J., Lasserson, T., Tovey, D., et al.: Methodological standards for the conduct of new cochrane intervention reviews. Cochrane Collaboration, Sl (2013)

    Google Scholar 

  6. Cohen, A.M.: Optimizing feature representation for automated systematic review work prioritization. In: AMIA Annu. Symp. Proceed. 2008, 121–125 (2008). American Medical Informatics Association (2008)

    Google Scholar 

  7. Cohen, A.M., Hersh, W.R., Peterson, K., Yen, P.Y.: Reducing workload in systematic review preparation using automated citation classification. J. Am. Med. Inf. Assoc. 13(2), 206–219 (2006)

    Google Scholar 

  8. De, L., De-Moor, B., Vandewalle, J.: On the best rank-1 and rank-(r1 r2...rn) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21(4), 1324–1342 (2000)

    Google Scholar 

  9. Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13–17 August 2017 (2017)

    Google Scholar 

  10. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proceedings of the National Academy Of Sciences (12) (2002)

    Google Scholar 

  11. Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. IEEE (2005)

    Google Scholar 

  12. Haddaway, N.R., Westgate, M.J.: Predicting the time needed for environmental systematic reviews and systematic maps. Conserv. Biol. 33, 434–443 (2018)

    Google Scholar 

  13. Harshman, R.A., et al.: Foundations of the parafac procedure: models and conditions for an “explanatory” multimodal factor analysis (1970)

    Google Scholar 

  14. Howard, B.E., et al.: Swift-review: a text-mining workbench for systematic review. Syst. Control Found. Appl. 5(1), 1–16 (2016)

    MathSciNet  Google Scholar 

  15. Khabsa, M., Elmagarmid, A., Ilyas, I., Hammady, H., Ouzzani, M.: Learning to identify relevant studies for systematic reviews using random forest and external information. Mach. Learn. 102(3), 465–482 (2015). https://doi.org/10.1007/s10994-015-5535-7

    Article  MathSciNet  Google Scholar 

  16. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of ICLR (2015)

    Google Scholar 

  17. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of ICLR (2017)

    Google Scholar 

  18. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)

    Google Scholar 

  19. Kontonatsios, G., et al.: A semi-supervised approach using label propagation to support citation screening. J. Biomed. Inf. 72, 67–76 (2017)

    Google Scholar 

  20. Lee, E.W., Wallace, B.C., Galaviz, K.I., Ho, J.C.: MMiDaS-AE: multi-modal missing data aware stacked autoencoder for biomedical abstract screening. In: Proceedings of the ACM Conference on Health, Inference, and Learning (2020)

    Google Scholar 

  21. Li, X., Wen, L., Qian, C., Wang, J.: GAHNE: graph-aggregated heterogeneous network embedding. In: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE (2020)

    Google Scholar 

  22. Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.S.: Gated graph sequence neural networks. In: Proceedings of ICLR (2016)

    Google Scholar 

  23. Ma, T., Xiao, C., Zhou, J., Wang, F.: Drug similarity integration through attentive multi-view graph auto-encoders. In: Proceedings of the Twenty-Seventh IJCAI 2018, Stockholm, Sweden (2018)

    Google Scholar 

  24. Miwa, M., Thomas, J., O’Mara-Eves, A., Ananiadou, S.: Reducing systematic review workload through certainty-based screening. J. Biomed. Inf. 51, 242–253 (2014)

    Google Scholar 

  25. Morris, Z.S., Wooding, S., Grant, J.: The answer is 17 years, what is the question: understanding time lags in translational research. J. Royal Soc. Med. 104(12), 510–520 (2011)

    Google Scholar 

  26. Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38(2), 321–330 (2004). https://doi.org/10.1140/epjb/e2004-00124-y

    Article  Google Scholar 

  27. Newman, M.E.: Modularity and community structure in networks. Proceed. Nat. Acad. Sci. 103(23), 8577–8582 (2006)

    Google Scholar 

  28. O’Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., Ananiadou, S.: Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst. Rev. 4(1), 5 (2015). https://doi.org/10.1186/2046-4053-4-5

  29. Papalexakis, E.E., Faloutsos, C., Sidiropoulos, N.D.: Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM Trans. Intell. Syst. Technol. (TIST) 8(2), 2915921 (2016)

    Google Scholar 

  30. Sarkar, S., Dong, A.: Community detection in graphs using singular value decomposition. Phys. Rev. E 83(4), 046114 (2011)

    Google Scholar 

  31. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2008)

    Google Scholar 

  32. Scells, H., Zuccon, G., Koopman, B., Deacon, A., Azzopardi, L., Geva, S.: A test collection for evaluating retrieval of studies for inclusion in systematic reviews. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1237–1240 (2017)

    Google Scholar 

  33. Shang, J., Qu, M., Liu, J., Kaplan, L.M., Han, J., Peng, J.: Meta-path guided embedding for similarity search in large-scale heterogeneous information networks. ArXiv preprint (2016)

    Google Scholar 

  34. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on WWW, Florence, Italy, 18–22 May 2015 (2015)

    Google Scholar 

  35. Tucker, L.R.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279–311 (1966). https://doi.org/10.1007/BF02289464

  36. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: Proceedings of ICLR (2018)

    Google Scholar 

  37. Wallace, B.C., Trikalinos, T.A., Lau, J., Brodley, C., Schmid, C.H.: Semi-automated screening of biomedical citations for systematic reviews. BMC Bioinf. 11(1), 1–11 (2010)

    Article  Google Scholar 

  38. Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13–17 May 2019 (2019)

    Google Scholar 

  39. Wanyan, T., Zhang, C., Azad, A., Liang, X., Li, D., Ding, Y.: Attribute2vec: Deep network embedding through multi-filtering GCN. ArXiv preprint (2020)

    Google Scholar 

  40. Yang, Y., Guan, Z., Li, J., Huang, J., Zhao, W.: Interpretable and efficient heterogeneous graph convolutional network. ArXiv preprint (2020)

    Google Scholar 

  41. Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8–14 Dec 2019, Vancouver, BC, Canada (2019)

    Google Scholar 

  42. Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4–8 Aug 2019 (2019)

    Google Scholar 

  43. Zhang, X., He, L., Chen, K., Luo, Y., Zhou, J., Wang, F.: Multi-view graph convolutional network and its applications on neuroimage analysis for Parkinson’s disease. In: AMIA Annu. Symp. Proceed. 2018, 1147–1156 (2018). American Medical Informatics Association (2018)

    Google Scholar 

Download references

Acknowledgements

We thank the reviewers for their insightful suggestions and comments. This work was supported by the National Science Foundation award IIS-1838200 and IIS-2145411.

Author information

Authors and Affiliations

  1. Emory University, Atlanta, GA, USA

    Eric W. Lee & Joyce C. Ho

Authors
  1. Eric W. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joyce C. Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eric W. Lee .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lee, E.W., Ho, J.C. (2023). SR-CoMbEr: Heterogeneous Network Embedding Using Community Multi-view Enhanced Graph Convolutional Network for Automating Systematic Reviews. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28244-7_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28243-0

  • Online ISBN: 978-3-031-28244-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation