id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-348803-2lix3a0u	Ikemura, K.	Using Automated-Machine Learning to Predict COVID-19 Patient Survival: Identify Influential Biomarkers	2020-10-14		.txt	text/plain	3789	263	57	In this study, we used automated machine learning (autoML) to develop and compare between multiple machine learning (ML) models that predict the chance of patient survival from COVID-19 infection and identified the best-performing model. Conclusions: By using autoML, we developed high-performing models that predict patient mortality from COVID-19 infection. In this study, we aimed to find the most important prognostic biomarkers and develop a COVID-19 mortality risk assessment tool using automated machine learning (autoML). We assigned the autoML to generate 20 machine learning models and rank them in order of performance by AUCPR on the remaining 20% of the dataset (859 patients, test set). After the two Stacked Ensemble models ranked GBM and XGBoost models with AUCPR of 0.830 and 0.825, respectively ( preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted October 14, 2020. We generated high-performing ML models that predicts mortality of COVID-19 infected patients using autoML.	./cache/cord-348803-2lix3a0u.txt	./txt/cord-348803-2lix3a0u.txt