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Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
[article]
2016
arXiv
pre-print
In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic
arXiv:1602.02685v2
fatcat:mlcg3fs73bda3mbnv5z5fkrma4