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A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
[article]
2020
arXiv
pre-print
Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. ISTS commonly appears in healthcare, economics, and geoscience. Especially in the medical environment, the widely used Electronic Health Records (EHRs) have abundant typical irregularly sampled medical time series (ISMTS) data. Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical
arXiv:2010.12493v2
fatcat:hyx3tab4svgptewwp6lhz2q6wy