Skip to main content

Advertisement

SpringerLink
Log in

Safe, effective and explainable drug recommendation based on medical data integration

  • Published:
User Modeling and User-Adapted Interaction Aims and scope Submit manuscript

Abstract

Medicine recommendation denotes the task of predicting drug combinations for patients’ therapies in case of complex diseases such as cancer or diabetes. These patients often follow a treatment consisting of multiple drugs simultaneously. Previous research works have already made predictions of next drug combinations based on the integration of the patients’ health records with an adverse drug–drug interaction (DDI) knowledge graph in order to minimize drug side effects. However, they missed to consider additional valuable information coming from synergistic drug–drug interaction knowledge graphs. In this paper, we integrate Electronic Health Record graph data incorporating patient, disease, therapy and drug information with either a synergistic or an adverse DDI knowledge graph to recommend safe and explainable medications. To the best of our knowledge, we are the first ones to compare three different medical data integration strategies based on the analysis stage (i.e. early, intermediate and late) at which integration takes place. By identifying those drugs that either complement each other or behave adversely, we are able to improve the efficacy of the patient’s therapy and/or minimize the toxicity and side effects. Moreover, we develop models to support doctors in comprehensively screening candidate drugs and their possible substitutes by providing also robust explanations alongside with the recommended medicine. We run experiments with three real-life medical data sets. In terms of suggesting safe and effective drugs, our proposed method suggests 34 times more synergistic drugs than the baseline algorithm for the cancer data set and reduces the unwanted side effects to patients’ medication by 2350 times more than the baseline algorithm for the MIMIC III data set. Our results demonstrate that we can assist doctors to prescribe effective, safe and explainable medication for the patients’ treatment.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Alaimo, S., Giugno, R., Pulvirenti, A.: Recommendation techniques for drug–target interaction prediction and drug repositioning. In: Data Mining Techniques for the Life Sciences, pp. 441–462. Springer (2016)

  • Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L.J., Bork, P.: Drug target identification using side-effect similarity. Science 321(5886), 263–266 (2008)

    Article  Google Scholar 

  • Celebi, R., Uyar, H., Yasar, E., Gumus, O., Dikenelli, O., Dumontier, M.: Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings. BMC Bioinform. 20(1), 1–14 (2019)

    Article  Google Scholar 

  • Cheng, F., Liu, C., Jiang, J., Lu, W., Li, W., Liu, G., Zhou, W., Huang, J., Tang, Y.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), e1002503 (2012)

    Article  Google Scholar 

  • 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, pp. 135–144 (2017)

  • Epure, E.V., Kille, B., Ingvaldsen, J.E., Deneckere, R., Salinesi, C., Albayrak, S.: Recommending personalized news in short user sessions. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 121–129. ACM (2017)

  • Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)

  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)

  • Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proceedings 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’2002), pp. 538–543, Edmonton, Canada (2002)

  • Karim, M.R., Cochez, M., Jares, J.B., Uddin, M., Beyan, O., Decker, S.: Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-lstm network. In: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 113–123 (2019)

  • Malyutina, A., Majumder, M.M., Wang, W., Pessia, A., Heckman, C.A., Tang, J.: Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. PLoS comput. Biol. 15(5), e1006752 (2019)

    Article  Google Scholar 

  • Peake, G., Wang, J.: Explanation mining: Post hoc interpretability of latent factor models for recommendation systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2060–2069 (2018)

  • Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW ’01, pp. 285–295, New York, NY, USA, ACM (2001)

  • Shang, J., Xiao, C., Ma, T., Li, H., Sun, J.: Gamenet: graph augmented memory networks for recommending medication combination. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33. pp. 1126–1133 (2019)

  • Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650–658, (2008)

  • Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)

    Article  Google Scholar 

  • Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Moviexplain: A recommender system with explanations. In: Proceedings of 3nd ACM Conference in Recommender Systems (RecSys’2009), pp. 317–320, New York, NY, (2009)

  • Szklarczyk, D., Santos, A., von Mering, C., Jensen, L.J., Bork, P., Kuhn, M.: Stitch 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44(D1), D380–D384 (2016)

    Article  Google Scholar 

  • Tatonetti, N.P., Patrick, P.Y., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4(125), 125ra31-125ra31 (2012)

    Article  Google Scholar 

  • Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: Sixth international conference on data mining (ICDM’06), pp 613–622. IEEE (2006)

  • Wang, M., Liu, M., Liu, J., Wang, S., Long, G., Qian, B.: Safe medicine recommendation via medical knowledge graph embedding. arXiv preprint arXiv:1710.05980 (2017)

  • Wang, S., Ren, P., Chen, Z., Ren, Z., Ma, J., de Rijke, M.: Order-free medicine combination prediction with graph convolutional reinforcement. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1623–1632 (2019)

  • Yizhou, S., Jiawei, H., Xifeng, Y., Philip S.Y., Tianyi, W.: Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. In :Proceedings of the VLDB Endowment (VLDB’2011), Seattle, Washington (2011)

  • Zagidullin, B., Aldahdooh, J., Zheng, S., Wang, W., Wang, Y., Saad, J., Malyutina, A., Jafari, M., Tanoli, Z., Pessia, A., et al.: Drugcomb: an integrative cancer drug combination data portal. Nucleic Acids Res 47(W1), W43–W51 (2019)

    Article  Google Scholar 

  • Zhang, Y., Chen, R., Tang, J., Stewart, W.F., Sun, J.: Leap: Learning to prescribe effective and safe treatment combinations for multimorbidity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1315–1324, (2017)

  • Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), i457–i466 (2018)

    Article  Google Scholar 

  • Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., Hoffman, M.M.: Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities. Inf. Fusion 50, 71–91 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Free University of Bozen-Bolzano, Bolzano, Italy

    Panagiotis Symeonidis, Stergios Chairistanidis & Markus Zanker

  2. University of Klagenfurt, Klagenfurt, Austria

    Panagiotis Symeonidis, Stergios Chairistanidis & Markus Zanker

  3. University of the Aegean, Samos, Greece

    Panagiotis Symeonidis

Authors
  1. Panagiotis Symeonidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stergios Chairistanidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Markus Zanker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Panagiotis Symeonidis.

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 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

Symeonidis, P., Chairistanidis, S. & Zanker, M. Safe, effective and explainable drug recommendation based on medical data integration. User Model User-Adap Inter 32, 999–1018 (2022). https://doi.org/10.1007/s11257-022-09342-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11257-022-09342-x

Keywords

Search

Navigation