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Deep Neural Networks for Prediction of Exacerbations of Patients with Chronic Obstructive Pulmonary Disease

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Engineering Applications of Neural Networks (EANN 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 893))

Abstract

Chronic Obstructive Pulmonary Disease (COPD) patients need help in daily life situations as they are burdened with frequent risks of acute exacerbation and loss of control. An automated monitoring system could lead to timely treatments and avoid unnecessary hospital (re-)admissions and home visits by doctors or nurses. Therefore we present a Deep Artificial Neural Networks for approach prediction of exacerbations, particularly Feed-Forward Neural Networks (FFNN) for classification of COPD patients category and Long Short-Term Memory (LSTM), for early prediction of COPD exacerbations and subsequent triage. The FFNN and LSTM models are trained on data collected from remote monitoring of 94 patients through a real monitoring session and therefore represents realistic home monitoring situations. Most deep learning models require large datasets in order to predict with a high degree of accuracy. Our experiments show that with only 94 patients, the FFNN model is able to reproduce health condition provided by a medical doctor with an accuracy of 92.86% and the LSTM model able to predict COPD patients’ health conditions one-day ahead with an accuracy of 84.12%. Based on our results, we believe that our work will help the medical doctors and nurses in identifying patients with acute exacerbation in advance which can lead to better patient care and decision making, and hence reduction of costs.

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Notes

  1. 1.

    This study was carried out on a different large dataset than our study and is therefore not comparable.

  2. 2.

    The experimental setup is available in https://github.com/cair/TELMA-Project.

  3. 3.

    Additional experiments without data-augmentation was carried out, but are for reasons of space deliberately left out of the paper. Without data-augmentation in the training set, the classifier performs badly.

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Acknowledgements

This work is part of an ongoing the Norwegian Research council project TELMA (Telemedicine in Agder) which develops a common solution for distance monitoring of patients with chronic diseases and comorbidity at Agder region to provide good health care with less use of health resources. We would like to thank Martin Wulf Gerdes and Prof. Rune Werner Fensli for sharing the COPD data.

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Authors and Affiliations

  1. Centre for Artificial Intelligence Research (CAIR), Department of ICT, University of Agder, Grimstad, Norway

    Vimala Nunavath, Morten Goodwin & Jahn Thomas Fidje

  2. Centre for E-Health Research, Department of IS, University of Agder, Kristiansand, Norway

    Carl Erik Moe

Authors
  1. Vimala Nunavath
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  2. Morten Goodwin
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  3. Jahn Thomas Fidje
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  4. Carl Erik Moe
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Corresponding author

Correspondence to Vimala Nunavath .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  2. Oxford Brookes University, Oxford, United Kingdom

    Chrisina Jayne

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Nunavath, V., Goodwin, M., Fidje, J.T., Moe, C.E. (2018). Deep Neural Networks for Prediction of Exacerbations of Patients with Chronic Obstructive Pulmonary Disease. In: Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-319-98204-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-98204-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98203-8

  • Online ISBN: 978-3-319-98204-5

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