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Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study
[article]
2019
arXiv
pre-print
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature
arXiv:1801.02961v2
fatcat:w4rqvzuvcza37hfpyqt46hazvq