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Authors: Veena Mayya 1 ; 2 ; Karthik K. 2 ; Sowmya S. Kamath 2 ; Krishnananda Karadka 3 and Jayakumar Jeganathan 4

Affiliations: 1 Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India ; 2 Healthcare Analytics and Language Engineering (HALE) Lab, Dept. of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India ; 3 Penzigo Technology Solutions Pvt. Ltd., NITK-Science and Technology Entrepreneurs’ Park (STEP), NITK Surathkal, India ; 4 Dept. of Medicine, Kasturba Medical College, Manipal Academy of Higher Education (MAHE), Karnataka, India

Keyword(s): Computational and Artificial Intelligence, Decision Support Systems, Automated Diagnosis, COVID-19.

Abstract: The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemb le deep learning models (CADNN) is designed and deployed on the weba. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions. (More)

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Paper citation in several formats:
Mayya, V.; K., K.; Kamath, S.; Karadka, K. and Jeganathan, J. (2021). COVIDDX: AI-based Clinical Decision Support System for Learning COVID-19 Disease Representations from Multimodal Patient Data. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - HEALTHINF; ISBN 978-989-758-490-9; ISSN 2184-4305, SciTePress, pages 659-666. DOI: 10.5220/0010341906590666

@conference{healthinf21,
author={Veena Mayya. and Karthik K.. and Sowmya S. Kamath. and Krishnananda Karadka. and Jayakumar Jeganathan.},
title={COVIDDX: AI-based Clinical Decision Support System for Learning COVID-19 Disease Representations from Multimodal Patient Data},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - HEALTHINF},
year={2021},
pages={659-666},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010341906590666},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - HEALTHINF
TI - COVIDDX: AI-based Clinical Decision Support System for Learning COVID-19 Disease Representations from Multimodal Patient Data
SN - 978-989-758-490-9
IS - 2184-4305
AU - Mayya, V.
AU - K., K.
AU - Kamath, S.
AU - Karadka, K.
AU - Jeganathan, J.
PY - 2021
SP - 659
EP - 666
DO - 10.5220/0010341906590666
PB - SciTePress