id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-229612-7xnredj7	Pal, Ankit	Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing	2020-10-06		.txt	text/plain	3617	225	51	An interpretable and COVID-19 diagnosis AI framework is devised and developed based on the cough sounds features and symptoms metadata to overcome these limitations. The proposed framework's performance was evaluated using a medical dataset containing Symptoms and Demographic data of 30000 audio segments, 328 cough sounds from 150 patients with four cough classes ( COVID-19, Asthma, Bronchitis, and Healthy). A three-layer Deep Neural Network model is used to generate cough embeddings from the handcrafted signal processing features and symptoms embeddings are generated by a transformer-based self-attention network called TabNet. Arik and Pfister (2020) Finally, the prediction score is obtained by concatenating the Symptoms Embeddings with Cough Embeddings, followed by a Fully Connected layer. • A novel explainable & interpretable COVID-19 diagnosis framework based on deep learning (AI) uses the information from symptoms and cough signal processing features.	./cache/cord-229612-7xnredj7.txt	./txt/cord-229612-7xnredj7.txt