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Identifying Patients at Risk for Aortic Stenosis Through Learning from Multimodal Data

  • Tanveer Syeda-MahmoodEmail author
  • Yufan Guo
  • Mehdi Moradi
  • D. Beymer
  • D. Rajan
  • Yu Cao
  • Yaniv Gur
  • Mohammadreza Negahdar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

In this paper we present a new method of uncovering patients with aortic valve diseases in large electronic health record systems through learning with multimodal data. The method automatically extracts clinically-relevant valvular disease features from five multimodal sources of information including structured diagnosis, echocardiogram reports, and echocardiogram imaging studies. It combines these partial evidence features in a random forests learning framework to predict patients likely to have the disease. Results of a retrospective clinical study from a 1000 patient dataset are presented that indicate that over 25 % new patients with moderate to severe aortic stenosis can be automatically discovered by our method that were previously missed from the records.

Supplementary material

432173_1_En_28_MOESM1_ESM.pptx (3.1 mb)
Supplementary material 1 (pptx 3187 KB)

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Copyright information

© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Tanveer Syeda-Mahmood
    • 1
    Email author
  • Yufan Guo
    • 1
  • Mehdi Moradi
    • 1
  • D. Beymer
    • 1
  • D. Rajan
    • 1
  • Yu Cao
    • 1
  • Yaniv Gur
    • 1
  • Mohammadreza Negahdar
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA

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