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Learning stochastic finite-state transducer to predict individual patient outcomes
2016
Health and Technology
The high frequency data in intensive care unit is flashed on a screen for a few seconds and never used again. However, this data can be used by machine learning and data mining techniques to predict patient outcomes. Learning finite-state transducers (FSTs) have been widely used in problems where sequences need to be manipulated and insertions, deletions and substitutions need to be modeled. In this paper, we learned the edit distance costs of a symbolic univariate time series representation
doi:10.1007/s12553-016-0146-2
pmid:27942425
pmcid:PMC5124435
fatcat:6yqc64yo4vejxb4tvxfxen2oyu