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2020 | OriginalPaper | Buchkapitel

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

verfasst von : Kezi Yu, Yunlong Wang, Yong Cai

Erschienen in: Advanced Analytics and Learning on Temporal Data

Verlag: 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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Vorheriges Kapitel Seq2VAR: Multivariate Time Series Representation with Relational Neural Networks and Linear Autoregressive Model
Nächstes Kapitel Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems
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Metadaten
Titel
Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets
verfasst von
Kezi Yu
Yunlong Wang
Yong Cai
Copyright-Jahr
2020
Buch
Advanced Analytics and Learning on Temporal Data

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

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

Copyright-Jahr: 2020

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

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