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Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications

Haohan Wang, Zhenglin Wu, Eric P. Xing
doi: https://doi.org/10.1101/442442
Haohan Wang
1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
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Zhenglin Wu
2School of Information Sciences, University of Illinois Urbana-Champaign Champaign, IL, USA
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Eric P. Xing
1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
3Petuum Inc. Pittsburgh, PA, USA
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Abstract

The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis. Recently, many deep learning methods have been shown with impressive successes in predicting disease status with raw input data. However, the “black-box” nature of deep learning and the high-reliability requirement of biomedical applications have created new challenges regarding the existence of confounding factors. In this paper, with a brief argument that inappropriate handling of confounding factors will lead to models’ sub-optimal performance in real-world applications, we present an efficient method that can remove the influences of confounding factors such as age or gender to improve the across-cohort prediction accuracy of neural networks. One distinct advantage of our method is that it only requires minimal changes of the baseline model’s architecture so that it can be plugged into most of the existing neu-ral networks. We conduct experiments across CT-scan, MRA, and EEG brain wave with convolutional neural networks and LSTM to verify the efficiency of our method.

Footnotes

  • E-mail: haohanw{at}cs.cmu.edu

  • To appear at Pacific Symposium on Biocomputing (PSB) 2019

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted October 13, 2018.
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Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
Haohan Wang, Zhenglin Wu, Eric P. Xing
bioRxiv 442442; doi: https://doi.org/10.1101/442442
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Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
Haohan Wang, Zhenglin Wu, Eric P. Xing
bioRxiv 442442; doi: https://doi.org/10.1101/442442

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