Phil 12.28.12

8:30 – 4:30 ASRC MKT

  • Still sick. Nearing bronchitis?
  • Confessions of a Digital Nazi Hunter
  • Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks
    • We present a novel application of LSTM recurrent neural networks to multi label classification of diagnoses given variable-length time series of clinical measurements. Our method outperforms a strong baseline on a variety of metrics.
    • Scholar Cited by
      • Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database
        • Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient’s record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patient’s interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers. We show that is possible to learn meaningful embeddings from these care events using two deep learning techniques, unsupervised autoencoders and long short-term memory networks. We compare these methods to traditional machine learning methods which require a point in time snapshot to be extracted from an EHR.
  • Continuing on white paper
  • Moved the Flocking and Herding paper over to the WSC17 format for editing. Will need to move to the WSC18 format when that becomes available