1 code implementation • 17 Apr 2021 • Yuqi Si, Kirk Roberts
We present a Three-level Hierarchical Transformer Network (3-level-HTN) for modeling long-term dependencies across clinical notes for the purpose of patient-level prediction.
no code implementations • 24 Feb 2021 • Yuqi Si, Elmer V Bernstam, Kirk Roberts
The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing.
no code implementations • 6 Oct 2020 • Yuqi Si, Jingcheng Du, Zhao Li, Xiaoqian Jiang, Timothy Miller, Fei Wang, W. Jim Zheng, Kirk Roberts
We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.
no code implementations • 30 Sep 2020 • Nicholas Greenspan, Yuqi Si, Kirk Roberts
Finally, we propose additional directions for research for improving extraction performance and utilizing the NLP system in downstream precision oncology applications.
no code implementations • 13 Aug 2019 • Surabhi Datta, Yuqi Si, Laritza Rodriguez, Sonya E Shooshan, Dina Demner-Fushman, Kirk Roberts
We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL).
no code implementations • 22 Feb 2019 • Yuqi Si, Jingqi Wang, Hua Xu, Kirk Roberts
We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings, and compare these on four concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015.
Ranked #1 on Clinical Concept Extraction on 2010 i2b2/VA