Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization

Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization. Ho, J. C., Ghosh, J., & Sun, J. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014, pages 115–124, 2014.

Paper doi bibtex

@inproceedings{DBLP:conf/kdd/HoGS14,
  author    = {Joyce C. Ho and
               Joydeep Ghosh and
               Jimeng Sun},
  title     = {Marble: high-throughput phenotyping from electronic health records
               via sparse nonnegative tensor factorization},
  booktitle = {The 20th {ACM} {SIGKDD} International Conference on Knowledge Discovery
               and Data Mining, {KDD} '14, New York, NY, {USA} - August 24 - 27,
               2014},
  pages     = {115--124},
  year      = {2014},
  crossref  = {DBLP:conf/kdd/2014},
  url       = {https://doi.org/10.1145/2623330.2623658},
  doi       = {10.1145/2623330.2623658},
  timestamp = {Tue, 06 Nov 2018 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/kdd/HoGS14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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