Advertisement

Multiple-Disease Risk Predictive Modeling Based on Directed Disease Networks

  • Tingyan Wang
  • Robin G. QiuEmail author
  • Ming Yu
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

This paper studies multiple-disease risk predictive models to assess a discharged patient’s future disease risks. We propose a novel framework that combines directed disease networks and recommendation system techniques to substantially enhance the performance of multiple-disease risk predictive modeling. Firstly, a directed disease network considering patients’ temporal information is developed. Then based on this directed disease network, we investigate different disease risk score computing approaches. We validate the proposed approaches using a hospital’s dataset. Promisingly, the predictive results can be well referenced by healthcare professionals who provide healthcare guidance for patients ready for discharge.

Keywords

Directed disease network Predictive modeling Multiple-disease risk assessment 

Notes

Acknowledgements

A significant part of this work from Tingyan Wang and Robin Qiu was done with the support from the Big Data Lab at Penn State. This project was partially supported by IBM Faculty Awards (RDP-Qiu2016 and RDP-Qiu2017).

References

  1. 1.
    M. Bayati, S. Bhaskar, A. Montanari, Statistical analysis of a low cost method for multiple disease prediction. Stat. Methods Med. Res. 27(8), 2312–2328 (2018)CrossRefGoogle Scholar
  2. 2.
    N.V. Chawla, D.A. Davis, Bringing big data to personalized healthcare: a patient-centered framework. J. Gen. Intern. Med. 28(3), 660–665 (2013)CrossRefGoogle Scholar
  3. 3.
    A. Chen, K.H. Jacobsen, A.A. Deshmukh, S.B. Cantor, The evolution of the disability-adjusted life year (DALY). Socio-Econ. Plann. Sci. 49, 10–15 (2015)CrossRefGoogle Scholar
  4. 4.
    D.A. Davis, N.V. Chawla, N. Blumm, N. Christakis, A.L. Barabasi, Predicting individual disease risk based on medical history. in Proceedings of the 17th ACM conference on Information and knowledge management, pp. 769–778 (2008)Google Scholar
  5. 5.
    F. Folino, C. Pizzuti, Link prediction approaches for disease networks. in International Conference on Information Technology in Bio-and Medical Informatics, (Springer, Berlin, Heidelberg, 2012), pp. 99–108CrossRefGoogle Scholar
  6. 6.
    F. Folino, C. Pizzuti, A recommendation engine for disease prediction. IseB 13(4), 609–628 (2015)CrossRefGoogle Scholar
  7. 7.
    A.J. Frandsen, Machine Learning for Disease Prediction, Master thesis (Brigham Young University, 2016)Google Scholar
  8. 8.
    T.H. Haveliwala, Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRefGoogle Scholar
  9. 9.
    J.L. Herlocker, J.A. Konstan, L.G. Terveen et al., Evaluating collaborative filtering recommender systems. ACM Trans. Info. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    V. Kannan, F. Swartz, N.A. Kiani, G. Silberberg, G. Tsipras, D. Gomez-Cabrero, K. Alexanderson, J. Tegnèr, Conditional disease development extracted from longitudinal health care cohort data using layered network construction. Sci. Rep. 6, 26170 (2016)CrossRefGoogle Scholar
  11. 11.
    R. Miotto, L. Li, B.A. Kidd, J.T. Dudley, Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016)CrossRefGoogle Scholar
  12. 12.
    M. Nasiri, B. Minaei, A. Kiani, Dynamic recommendation: Disease prediction and prevention using recommender system. Int. J. Basic Sci. Med. 1(1), 13–17 (2016)CrossRefGoogle Scholar
  13. 13.
    J.A. Paul, L. MacDonald, G. Hariharan, Modeling risk factors and disease conditions to study associated lifetime medical costs. Serv. Sci. 6(1), 47–62 (2014)CrossRefGoogle Scholar
  14. 14.
    S. Selvarajah, G. Kaur, J. Haniff, K.C. Cheong, T.G. Hiong, Y. van der Graaf, M.L. Bots, Comparison of the Framingham risk score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. Int. J. Cardiol. 176(1), 211–218 (2014)CrossRefGoogle Scholar
  15. 15.
    World Health Organization (2019) International statistical classification of diseases and related health problems, 10th Revision. Retrieved 8 Jan 2019. http://apps.who.int/classifications/icd10/browse/2016/en
  16. 16.
    C. Willi, P. Bodenmann, W.A. Ghali, P.D. Faris, J. Cornuz, Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 298(22), 2654–2664 (2007)CrossRefGoogle Scholar
  17. 17.
    M.L. Zhang, Z.H. Zhou, A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Nuffield Department of MedicineUniversity of OxfordOxfordUK
  2. 2.Big Data Lab, Division of Engineering and Information ScienceThe Pennsylvania State UniversityMalvernUSA
  3. 3.Department of Industrial EngineeringHealth Care Services Research Centre, Tsinghua UniversityBeijingChina

Personalised recommendations