Skip to main content

2020 | OriginalPaper | Buchkapitel

Multiple-Disease Risk Predictive Modeling Based on Directed Disease Networks

verfasst von : Tingyan Wang, Robin G. Qiu, Ming Yu

Erschienen in: Smart Service Systems, Operations Management, and Analytics

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat 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)CrossRef 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)CrossRef
2.
Zurück zum Zitat 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)CrossRef 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)CrossRef
3.
Zurück zum Zitat 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)CrossRef 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)CrossRef
4.
Zurück zum Zitat 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) 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)
5.
Zurück zum Zitat 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. 99–108CrossRef 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. 99–108CrossRef
6.
Zurück zum Zitat F. Folino, C. Pizzuti, A recommendation engine for disease prediction. IseB 13(4), 609–628 (2015)CrossRef F. Folino, C. Pizzuti, A recommendation engine for disease prediction. IseB 13(4), 609–628 (2015)CrossRef
7.
Zurück zum Zitat A.J. Frandsen, Machine Learning for Disease Prediction, Master thesis (Brigham Young University, 2016) A.J. Frandsen, Machine Learning for Disease Prediction, Master thesis (Brigham Young University, 2016)
8.
Zurück zum Zitat T.H. Haveliwala, Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRef T.H. Haveliwala, Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)CrossRef
9.
Zurück zum Zitat 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)CrossRef 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)CrossRef
10.
Zurück zum Zitat 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)CrossRef 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)CrossRef
11.
Zurück zum Zitat 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)CrossRef 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)CrossRef
12.
Zurück zum Zitat 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)CrossRef 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)CrossRef
13.
Zurück zum Zitat 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)CrossRef 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)CrossRef
14.
Zurück zum Zitat 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)CrossRef 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)CrossRef
16.
Zurück zum Zitat 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)CrossRef 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)CrossRef
17.
Zurück zum Zitat M.L. Zhang, Z.H. Zhou, A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRef M.L. Zhang, Z.H. Zhou, A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRef
Metadaten
Titel
Multiple-Disease Risk Predictive Modeling Based on Directed Disease Networks
verfasst von
Tingyan Wang
Robin G. Qiu
Ming Yu
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-30967-1_21