Mayya, V.Karthik, K.Karadka, K.P.Kamath S․, S.S.2026-02-042023International Journal of Medical Engineering and Informatics, 2023, 15, 6, pp. 501-51517550653https://doi.org/10.1504/IJMEI.2023.134537https://idr.nitk.ac.in/handle/123456789/22094Over the continued course of the COVID-19 pandemic, a significant volume of expert-written diagnosis reports has been accumulated that capture a multitude of symptoms and observations on diagnosed COVID-19 cases, along with expert-validated chest X-ray scans. The utility of rich, latent information embedded in such unstructured expert-written diagnosis reports and its importance as a source of valuable disease-specific information has been explored to a very limited extent. In this work, a convolutional attention-based dense (CAD) neural model for COVID-19 prediction is proposed. The model is trained on the rich disease-specific parameters extracted from chest X-ray images and expert-written diagnostic text reports to support an evidence-based diagnosis. Scalability is ensured by incorporating content based learning models for automatically generating diagnosis reports of identified COVID-19 cases, reducing radiologists' cognitive burden. Experimental evaluation showed that multimodal patient data plays a vital role in diagnosing early-stage cases, thus helping hasten the diagnosis process. © 2023 Inderscience Enterprises Ltd.accuracyArticleattentionclinical evaluationconvolutional neural networkcoronavirus disease 2019data analysisdecision treedeep neural networkhumanimage retrievallearninglearning algorithmmultimodal datapredictionsensitivity and specificitysupport vector machinethorax radiographytraining