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A Recommender System of Medical Reports Leveraging Cognitive Computing and Frame Semantics

  • Danilo DessìEmail author
  • Diego Reforgiato Recupero
  • Gianni Fenu
  • Sergio Consoli
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 149)

Abstract

During the last decades, a huge amount of data have been collected in clinical databases in the form of medical reports, laboratory results, treatment plans, etc., representing patients health status. Hence, digital information available for patient-oriented decision making has increased drastically but it is often not mined and analyzed in depth since: (i) medical documents are often unstructured and therefore difficult to analyze automatically, (ii) doctors traditionally rely on their experience to recognize an illness, give a diagnosis, and prescribe medications. However doctors experience can be limited by the cases they are treated so far and medication errors can occur frequently. In addition, it is generally hard and time-consuming inferring information for comparing unstructured data and evaluating similarities between heterogeneous resources. Technologies as Data Mining, Natural Language Processing, and Machine Learning can provide possibilities to explore and exploit potential knowledge from diagnosis history records and help doctors to prescribe medication correctly to decrease medication error effectively. In this paper, we design and implement a medical recommender system that is able to cluster a collection of medical reports on features detected by IBM Watson and Framester, two emerging tools from, respectively, Cognitive Computing and Frame Semantics, and then, giving a medical report from a specific patient as input, to recommend similar other medical reports from patients who had analogues symptoms. Experiments and results have proved the quality of the resulting clustering and recommendations, and the key role that these innovative services can play on the biomedical sector. The proposed system is able to classify new medical cases thus supporting physicians to take more correct and reliable actions about specific diagnosis and cares.

Keywords

Health recommender systems Data mining Cognitive computation Personal health records Clustering Knowledge inference Personalized medicine Relevance computation Biomedical text-mining 

Notes

Acknowledgements

Danilo Dessì gratefully acknowledges Sardinia Regional Government for the financial support of his PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020—Axis III Education and training, Thematic goal 10, Priority of investment 10ii, Specific goal 10.5).

References

  1. 1.
    Mishra, R., Bian, J., Fiszman, M., Weir, C.R., Jonnalagadda, S., Mostafa, J., Del Fiol, G.: Text summarization in the biomedical domain: a systematic review of recent research. J. biomed. Inform. 52, 457–467 (2014)CrossRefGoogle Scholar
  2. 2.
    Sezgin, E., Ozkan, S.: A systematic literature review on health recommender systems. In: IEEE E-Health and Bioengineering Conference (EHB), pp. 1–4 (2013)Google Scholar
  3. 3.
    de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Recommender Systems Handbook, pp. 119–159. Springer (2015)Google Scholar
  4. 4.
    Capelle, M., Hogenboom, F., Hogenboom, A., Frasincar, F.: Semantic news recommendation using wordnet and bing similarities. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 296–302. ACM (2013)Google Scholar
  5. 5.
    Lin, D.: Review of “WordNet: an electronic lexical database” by Christiane Fellbaum. The MIT Press 1998. Comput. Linguist. 25(2), 292–296 (1999)MathSciNetGoogle Scholar
  6. 6.
    Baker, F.C., Fillmore, C.J., Lowe, J.B.: The berkeley framenet project. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics, ACL ’98 and 17th International Conference on Computational Linguistics, vol. 1, pp. 86–90. Association for Computational Linguistics, Stroudsburg, PA, USA (1998)Google Scholar
  7. 7.
    Gangemi, A., Alam, M., Asprino, L., Presutti, V., Recupero, D.R.: Framester: a wide coverage linguistic linked data hub. In: 2016 20th International Conference on Proceedings of Knowledge Engineering and Knowledge Management, EKAW, pp. 239–254. Springer (2016)Google Scholar
  8. 8.
    Navigli, R., Ponzetto, S.P.: Babelnet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bleik, S., Mishra, M., Huan, J., Song, M.: Text categorization of biomedical data sets using graph kernels and a controlled vocabulary. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(5), 1211–1217 (2013)CrossRefGoogle Scholar
  10. 10.
    Cohen, A.M., Hersh, W.R.: A survey of current work in biomedical text mining. Brief. bioinform. 6(1), 57–71 (2005)CrossRefGoogle Scholar
  11. 11.
    Toor, R., Chana, I.: Application of IT in healthcare: a systematic review. ACM SIGBioinform. Rec. 6(2), 1–8 (2016)CrossRefGoogle Scholar
  12. 12.
    Presutti, V., Consoli, S., Nuzzolese, A.G., Recupero, D.R., Gangemi, A., Bannour, I., Zargayouna, H.: Uncovering the semantics of wikipedia pagelinks. In: Lecture Notes in Computer Science, vol. 8876, pp. 413–428 (2014)Google Scholar
  13. 13.
    Presutti, V., Nuzzolese, A.G., Consoli, S., Gangemi, A., Recupero, D.R.: From hyperlinks to semantic web properties using open knowledge extraction. Semant. Web 7(4), 351–378 (2016)CrossRefGoogle Scholar
  14. 14.
    Lushnov, M., Safin, T., Lapaev, M., Zhukova, N.: Medical text processing for SMDA project. In: EMSA-RMed@ESWC (2016)Google Scholar
  15. 15.
    Consoli, S., Stilianakis, N.I.: A quartet method based on variable neighbourhood search for biomedical literature extraction and clustering. Int. Trans. Oper. Res. 24(3), 537–558 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Chernyshevich, M., Stankevitch, V.: IHS-RD-BELARUS: clinical named entities identification in French medical texts. Physiology 279, 291 (2015)Google Scholar
  17. 17.
    Dessì, D., Recupero, D.R., Fenu, G., Consoli, S.: Exploiting cognitive computing and frame semantic features for biomedical document clustering. In: Proceedings of the Workshop on Semantic Web Solutions for Large-scale Biomedical Data Analytics co-located with 14th Extended Semantic Web Conference, SeWeBMeDA@ESWC 2017, pp. 20–34 (2017)Google Scholar
  18. 18.
    Yeh, A.S., Hirschman, L., Morgan, A.A.: Evaluation of text data mining for database curation: lessons learned from the KDD challenge cup. Bioinformatics 19(Suppl. 1), 331–339 (2003)CrossRefGoogle Scholar
  19. 19.
    Regev, Y., Finkelstein-Landau, M., Feldman, R.: Rule-based extraction of experimental evidence in the biomedical domain: the KDD cup 2002 (task 1). ACM SIGKDD Explor. Newslett. 4(2), 90–92 (2002)CrossRefGoogle Scholar
  20. 20.
    Donaldson, I., Martin, J., de Bruijn, B., Wolting, C., Lay, V., Tuekam, B., Zhang, S., Baskin, B., Bader, G.D., Michalickova, K., Pawson, T., Hogue, C.W.V.: PreBIND and textomy—mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinform. 4(1), 11 (2003)CrossRefGoogle Scholar
  21. 21.
    Shehata, S., Karray, F., Kamel, M.: An efficient concept-based mining model for enhancing text clustering. IEEE Trans. Knowl. Data Eng. 22(10), 1360–1371 (2010)CrossRefGoogle Scholar
  22. 22.
    Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Recommender Systems Handbook, pp. 73–105. Springer (2011)Google Scholar
  23. 23.
    Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits wordnet-based user profiles for neighborhood formation. User Model. User-Adapt. Interact. 17(3), 217–255 (2007)CrossRefGoogle Scholar
  24. 24.
    Gu, J., Feng, W., Zeng, J., Mamitsuka, H., Zhu, S.: Efficient semisupervised MEDLINE document clustering with MeSH-semantic and global-content constraints. IEEE Trans. Cybern. 43(4), 1265–1276 (2013)CrossRefGoogle Scholar
  25. 25.
    Bromuri, S., Zufferey, D., Hennebert, J., Schumacher, M.: Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms. J. Biomed. Inform. 51, 165–175 (2014)CrossRefGoogle Scholar
  26. 26.
    Marafino, B.J., Davies, J.M., Bardach, N.S., Dean, M.L., Dudley, R.A., Boscardin, J.: N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. J. Am. Med. Inform. Assoc. 21(5), 871–875 (2014)CrossRefGoogle Scholar
  27. 27.
    Hughes, M., Li, I., Kotoulas, S., Suzumura, T.: Medical text classification using convolutional neural networks. Stud. Health Technol. Inform. 235, 246–50 (2017)Google Scholar
  28. 28.
    Zhao, R.W., Li, G.Z., Liu, J.M., Wang, X.: Clinical multi-label free text classification by exploiting disease label relation. In: 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 311–315. IEEE (2013)Google Scholar
  29. 29.
    Glinka, K., Woźniak, R., Zakrzewska, D.: Improving multi-label medical text classification by feature selection. In: 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 176–181. IEEE (2017)Google Scholar
  30. 30.
    Baumel, T., Nassour-Kassis, J., Elhadad, M., Elhadad, N.: Multi-label classification of patient notes a case study on icd code assignment. CoRR abs/1709.09587 (2017)Google Scholar
  31. 31.
    Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E.D., Gutierrez, J.B., Kochut, K.: A Brief Survey of Text Mining: classification, clustering and extraction techniques. arXiv:1707.02919 (2017)
  32. 32.
    Zhang, X., Jing, L., Hu, X., Ng, M., Xia, J., Zhou, X.: Medical Document Clustering using Ontology-based Term Similarity Measures (2008)Google Scholar
  33. 33.
    Zhang, Y., He, Z., Yang, J.J., Wang, Q., Li, J.: Re-structuring and specific similarity computation of electronic medical records. In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 230–235. IEEE (2017)Google Scholar
  34. 34.
    Chen, A.T.: Exploring online support spaces: using cluster analysis to examine breast cancer, diabetes and fibromyalgia support groups. Patient Educ. Couns. 87(2), 250–257 (2012)CrossRefGoogle Scholar
  35. 35.
    Lu, Y., Zhang, P., Deng, S.: Exploring health-related topics in online health community using cluster analysis. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 802–811. IEEE (2013)Google Scholar
  36. 36.
    Wiesner, M., Pfeifer, D.: Adapting recommender systems to the requirements of personal health record systems. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 410–414. ACM (2010)Google Scholar
  37. 37.
    Davis, D.A., Chawla, N.V., Blumm, N., Christakis, N., Barabási, A.L.: Predicting individual disease risk based on medical history. In: Proceedings of the 17th ACM Conference On Information and Knowledge Management, pp. 769–778. ACM (2008)Google Scholar
  38. 38.
    Zhang, Y., Chen, M., Huang, D., Wu, D., Li, Y.: iDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Gener. Comput. Syst. 66, 30–35 (2017)CrossRefGoogle Scholar
  39. 39.
    Fillmore, C.: Frame semantics. In: Linguistics in the Morning Calm, pp. 111–137 (1982)Google Scholar
  40. 40.
    Gangemi, A.: What’s in a Schema? pp. 144–182, Cambridge University Press, Cambridge (2010)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Danilo Dessì
    • 1
    Email author
  • Diego Reforgiato Recupero
    • 1
  • Gianni Fenu
    • 1
  • Sergio Consoli
    • 2
  1. 1.Mathematics and Computer Science DepartmentUniversity of CagliariCagliariItaly
  2. 2.Philips Research, Data Science DepartmentEindhovenThe Netherlands

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