Corpus ID: 53284711

Learning Representations of Missing Data for Predicting Patient Outcomes

@article{Malone2018LearningRO,
  title={Learning Representations of Missing Data for Predicting Patient Outcomes},
  author={Brandon M. Malone and Alberto Garc{\'i}a-Dur{\'a}n and Mathias Niepert},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.04752}
}
  • Brandon M. Malone, Alberto García-Durán, Mathias Niepert
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, such as multivariate time series, free text, and categorical demographic information; important relationships among patients can be difficult to detect; and many others. In this work, we propose a novel approach to address these first three challenges using a… CONTINUE READING
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    Multitask learning and benchmarking with clinical time series data

    VIEW 16 EXCERPTS
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