Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 25862765

Abstract

OBJECTIVE: Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability.

Authors

  • Yuan Luo
    Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Yu Xin
    Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, School of Biotechnology, Jiangnan University, Wuxi, China.
  • Ephraim Hochberg
    Center for Lymphoma, Massachusetts General Hospital and Department of Medicine, Harvard Medical School.
  • Rohit Joshi
    Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology.
  • Ozlem Uzuner
    Department of Information Studies, University at Albany, SUNY. Albany, NY.
  • Peter Szolovits
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.

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