id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-102850-0kiypige	Huang, C.-C.	A Machine Learning Study to Improve Surgical Case Duration Prediction	2020-06-12		.txt	text/plain	4728	252	53	The results are reported in 225 In Fig. 3 , we plotted scatter plots of actual versus predicted duration on the external 234 testing set for the average models of surgeon-and procedure-specific, and the XGB 235 model. Moreover, 251 three of the features which we computed from surgeons' data (i.e. total surgical minutes 252 performed by the surgeon within the last 7 days and on the same day, and number of Accurate prediction of operation case duration is vital in elevating OR efficiency and 257 reducing cost. It has been reported in the past studies that primary surgeons contributed the 301 largest variability in operation case duration prediction compared to other factors 302 attributed to patients [2, 16, 23] . 356 We propose extracting additional information from operation and surgeons' data to 357 be used as predictor variables for ML algorithm training since their importance was 358 high in the XGB model.	./cache/cord-102850-0kiypige.txt	./txt/cord-102850-0kiypige.txt