id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-012462-q8u47hdp	Olsavszky, Victor	Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database	2020-07-10		.txt	text/plain	5247	267	49	By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. For this purpose the corresponding ICD-10 codes for ischemic heart diseases, stroke, chronic obstructive pulmonary disease, lower respiratory infections, Alzheimer's disease, lung cancer, diabetes mellitus, road injuries, diarrheal diseases, and tuberculosis (Table S1) were extracted from the whole ICD-10 data set of hospitalized patients in Romania from the period 2008-2018. Another reduction in case counts is observed with chronic obstructive pulmonary disease, especially in the North East region, when comparing the predicted years to the previous ones ( Figure 4C ). When compared to the current literature, this is the first study on a national ICD-10 database to perform thorough time series forecasting on multiple diseases on a regional level using AutoML to select the most accurate of a multitude of models (Table S5) .	./cache/cord-012462-q8u47hdp.txt	./txt/cord-012462-q8u47hdp.txt