Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract)

Jiaming Zeng, Imon Banerjee, Michael Francis Gensheimer, Daniel L. Rubin

AAAI 2020 pp. 13981-13982

doi:10.1609/AAAI.V34I10.7263 /aaai/2020/zeng2020aaai-cancer/

Abstract

We built a natural language processing (NLP) language model that can be used to extract cancer treatment information using structured and unstructured electronic medical records (EMR). Our work appears to be the first that combines EMR and NLP for treatment identification.

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Cite

Text

Zeng et al. "Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7263

Markdown

[Zeng et al. "Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/zeng2020aaai-cancer/) doi:10.1609/AAAI.V34I10.7263

BibTeX

@inproceedings{zeng2020aaai-cancer,
  title     = {{Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract)}},
  author    = {Zeng, Jiaming and Banerjee, Imon and Gensheimer, Michael Francis and Rubin, Daniel L.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13981-13982},
  doi       = {10.1609/AAAI.V34I10.7263},
  url       = {https://mlanthology.org/aaai/2020/zeng2020aaai-cancer/}
}