id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-204835-1yay69kq	Sun, Chenxi	A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data	2020-10-23		.txt	text/plain	8291	567	55	title: A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. Recurrent neural networks (RNNs) [25, 26, 27] , auto-encoder (AE) [28, 29] and generative adversarial networks (GANs) [30, 31] have achieved good performance in medical data imputation and medical prediction thanks to their abilities of learning and generalization obtained by complex nonlinearity. End-to-end approaches process the downstream tasks directly based on modeling the time series with missing data. According to the analysis of technologies and experiment results, in this section, we will discuss ISMTS modeling task from three perspectives -1) imputation task with prediction task, 2) intra-series relation with inter-series relation / local structure with global structure and 3) missing data with raw data. Thus, of particular interest are irregularity-based methods that can learn directly by using multivariate sparse and irregularly sampled time series as input without the need for other imputation.	./cache/cord-204835-1yay69kq.txt	./txt/cord-204835-1yay69kq.txt