Injecting Domain Knowledge in Electronic Medical Records to Improve Hospitalization Prediction

  • Raphaël GazzottiEmail author
  • Catherine Faron-Zucker
  • Fabien Gandon
  • Virginie Lacroix-Hugues
  • David Darmon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)


Electronic medical records (EMR) contain key information about the different symptomatic episodes that a patient went through. They carry a great potential in order to improve the well-being of patients and therefore represent a very valuable input for artificial intelligence approaches. However, the explicit knowledge directly available through these records remains limited, the extracted features to be used by machine learning algorithms do not contain all the implicit knowledge of medical expert. In order to evaluate the impact of domain knowledge when processing EMRs, we augment the features extracted from EMRs with ontological resources before turning them into vectors used by machine learning algorithms. We evaluate these augmentations with several machine learning algorithms to predict hospitalization. Our approach was experimented on data from the PRIMEGE PACA database that contains more than 350,000 consultations carried out by 16 general practitioners (GPs).


Predictive model Electronic medical record Knowledge graph 



This work is partly funded by the French government labelled PIA program under its IDEX UCAJEDI project (ANR-15-IDEX-0001).


  1. 1.
    Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  3. 3.
    Cawley, G.C., Talbot, N.L.: On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  5. 5.
    Choi, E., et al.: GRAM: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795. ACM (2017)Google Scholar
  6. 6.
    Corby, O., Zucker, C.F.: The KGRAM abstract machine for knowledge graph querying. In: Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 338–341. IEEE (2010)Google Scholar
  7. 7.
    Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems (I-Semantics) (2013)Google Scholar
  8. 8.
    Forman, G., Scholz, M.: Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. ACM SIGKDD Explor. Newsl. 12(1), 49–57 (2010)CrossRefGoogle Scholar
  9. 9.
    Goldstein, B.A., Navar, A.M., Pencina, M.J., Ioannidis, J.: Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J. Am. Med. Inform. Assoc. 24(1), 198–208 (2017)CrossRefGoogle Scholar
  10. 10.
    Lacroix-Hugues, V., Darmon, D., Pradier, C., Staccini, P.: Creation of the first french database in primary care using the ICPC2: feasibility study. Stud. Health Technol. Inform. 245, 462–466 (2017)Google Scholar
  11. 11.
    McCullagh, P., Nelder, J.A.: Generalized Linear Models, vol. 37. CRC Press, Boca Raton (1989)CrossRefGoogle Scholar
  12. 12.
    Min, H., Mobahi, H., Irvin, K., Avramovic, S., Wojtusiak, J.: Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology. J. Biomed. Semant. 8(1), 39 (2017)CrossRefGoogle Scholar
  13. 13.
    Ordónez, F.J., de Toledo, P., Sanchis, A.: Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13(5), 5460–5477 (2013)CrossRefGoogle Scholar
  14. 14.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  16. 16.
    Salguero, A.G., Espinilla, M., Delatorre, P., Medina, J.: Using ontologies for the online recognition of activities of daily living. Sensors 18(4), 1202 (2018)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Université Côte d’Azur, Inria, CNRS, I3SSophia-AntipolisFrance
  2. 2.Université Côte d’Azur, Département de Médecine GénéraleNiceFrance
  3. 3.SynchroNextNiceFrance

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