TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-based Memory Network

Author:

Theodorou Brandon Philip1ORCID,Xiao Cao2ORCID,Sun Jimeng1ORCID

Affiliation:

1. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

2. GE Healthcare, Chicago, Illinois, USA

Funder

NSF

Publisher

ACM

Reference16 articles.

1. Anita Alicante , Anna Corazza , Francesco Isgro , and Stefano Silvestri . 2016. Unsu-pervised entity and relation extraction from clinical records in Italian. Computers in biology and medicine 72 ( 2016 ), 263--275. Anita Alicante, Anna Corazza, Francesco Isgro, and Stefano Silvestri. 2016. Unsu-pervised entity and relation extraction from clinical records in Italian. Computers in biology and medicine 72 (2016), 263--275.

2. Emily Alsentzer , John R Murphy , Willie Boag , Wei-Hung Weng , Di Jin , Tristan Naumann , and Matthew McDermott . 2019. Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323 ( 2019 ). Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323 (2019).

3. Learning Eligibility in Cancer Clinical Trials Using Deep Neural Networks

4. IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

5. Frederik Diehl and Alois Knoll . 2019 . Tree Memory Networks for Sequence Processing. In International Conference on Artificial Neural Networks. Springer, 431--443 . Frederik Diehl and Alois Knoll. 2019. Tree Memory Networks for Sequence Processing. In International Conference on Artificial Neural Networks. Springer, 431--443.

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