COVIDDX: AI-based Clinical Decision Support System for Learning COVID-19 Disease Representations from Multimodal Patient Data

Veena Mayya, Veena Mayya, Karthik K., Sowmya Kamath, Krishnananda Karadka, Jayakumar Jeganathan

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 ensemble 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.

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Paper Citation


in Harvard Style

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 - Volume 4: HEALTHINF, ISBN 978-989-758-490-9, pages 659-666. DOI: 10.5220/0010341906590666


in Bibtex Style

@conference{healthinf21,
author={Veena Mayya and Karthik K. and Sowmya 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 - Volume 4: HEALTHINF,},
year={2021},
pages={659-666},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010341906590666},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: 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
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