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Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets

  • 2020
  • OriginalPaper
  • Chapter
Published in:
Authors
Kezi Yu
Yunlong Wang
Yong Cai
Published in
Advanced Analytics and Learning on Temporal Data
Publisher
Springer International Publishing

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Abstract

Rare diseases affect 350 million patients worldwide, but they are commonly delayed in diagnosis or misdiagnosed. The problem of detecting rare disease faces two main challenges: the first being extreme imbalance of data and the second being finding the appropriate features. In this paper, we propose to address the problems by using semi-supervised generative adversarial networks (GANs) to deal with the data imbalance issue and recurrent neural networks (RNNs) to directly model patient sequences. We experimented with detecting patients with a particular rare disease (exocrine pancreatic insufficiency, EPI). The dataset includes 1.8 million patients with 29,149 patients being positive, from a large longitudinal study using 7 years medical claims. Our model achieved 0.56 PR-AUC and outperformed benchmark models in terms of precision and recall.

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previous chapter Seq2VAR: Multivariate Time Series Representation with Relational Neural Networks and Linear Autoregressive Model
next chapter Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems
Title
Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets
Authors
Kezi Yu
Yunlong Wang
Yong Cai
Copyright Year
2020
Book
Advanced Analytics and Learning on Temporal Data

Print ISBN: 978-3-030-39097-6

Electronic ISBN: 978-3-030-39098-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39098-3_11
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