Predicting glaucoma progression using multi-task learning with heterogeneous features

Shigeru Maya, Kai Morino, Kenji Yamanishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

We consider the prediction of glaucomatous visual field loss based on patient datasets. It is critically important to predict how rapidly the disease is progressing in an individual patient. However, the number of measurements for each patient is so small that a reliable predictor cannot be constructed from the data of a single patient alone. In this paper, we propose a novel multi-task learning approach to this issue. Patient data consist of three features: patient ID, 74-dimensional visual loss values, and inspection time. We reduce the prediction problem into one of matrix completion for these features. Specifically, by assuming heterogeneity in the three features, we introduce similarity measures that reflect the unique statistical nature of the respective features to solve a specific type of matrix decomposition problem. For example, we employ Gaussian kernels as a similarity measure for visual field loss and a linear regression-type relation for the time feature. We empirically demonstrate that our proposed method works significantly better than the existing methods.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-270
Number of pages10
ISBN (Electronic)9781479956654
DOIs
Publication statusPublished - Jan 7 2015
Externally publishedYes
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: Oct 27 2014Oct 30 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
CountryUnited States
CityWashington
Period10/27/1410/30/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

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