id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-298915-g1u9jods	Chen, Y.	An Interpretable Machine Learning Framework for Accurate Severe vs Non-severe COVID-19 Clinical Type Classification	2020-05-22		.txt	text/plain	4171	262	51	Currently, severe and non-severe COVID-19 types are differentiated by only a few clinical features, which do not comprehensively characterize complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. Machine learning random forest (RF) models using features in each modality were developed and validated to classify COVID-19 clinical types. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patient's severity and developing treatment plans accordingly. This study delivers an accurate diagnostic decision support tool to differentiate non-111 severe from severe type patients based on commonly available clinical data with clear clinical 112 Therefore, we developed an end-to-end ML framework to accurately 422 predict COVID-19 patient's clinical type based on symptom and/or biochemistry modality 423 features. The goal of ML classification through RF was to accurately predict the patient's COVID-19 type, 434 either "positive" (severe) or "negative" (non-severe), based on features from different clinical 435 modalities.	./cache/cord-298915-g1u9jods.txt	./txt/cord-298915-g1u9jods.txt