no code implementations • 24 Jun 2021 • Xianlong Zeng, Simon Lin, Chang Liu
In addition, our framework showed a great generalizability potential to transfer learned knowledge from one institution to another, paving the way for future healthcare model pre-training across institutions.
no code implementations • 23 Jun 2021 • Xianlong Zeng, Simon Lin, Chang Liu
The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level.
2 code implementations • 30 Oct 2020 • Xiang Yue, Xinliang Frederick Zhang, Ziyu Yao, Simon Lin, Huan Sun
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts.
1 code implementation • EMNLP 2021 • Xinliang Frederick Zhang, Heming Sun, Xiang Yue, Simon Lin, Huan Sun
For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1, 236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.
no code implementations • WS 2020 • Manirupa Das, Juanxi Li, Eric Fosler-Lussier, Simon Lin, Steve Rust, Yungui Huang, Rajiv Ramnath
Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature.
1 code implementation • ACL 2020 • Zhen Wang, Jennifer Lee, Simon Lin, Huan Sun
Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain.
no code implementations • 11 Nov 2019 • Manirupa Das, Juanxi Li, Eric Fosler-Lussier, Simon Lin, Soheil Moosavinasab, Steve Rust, Yungui Huang, Rajiv Ramnath
Our approach to generate document encodings employing our sequence-to-set models for inference of semantic tags, gives to the best of our knowledge, the state-of-the-art for both, the unsupervised query expansion task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25--based document retrieval system; and also over the MLTM baseline (Soleimani et al, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del. icio. us and Ohsumed datasets.
no code implementations • 13 Sep 2019 • Xianlong Zeng, Soheil Moosavinasab, En-Ju D Lin, Simon Lin, Razvan Bunescu, Chang Liu
Efficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction.
1 code implementation • 21 Jun 2019 • Zhen Wang, Xiang Yue, Soheil Moosavinasab, Yungui Huang, Simon Lin, Huan Sun
To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i. e., the surface form information, and the global context information for synonym discovery.
no code implementations • WS 2018 • Manirupa Das, Eric Fosler-Lussier, Simon Lin, Soheil Moosavinasab, David Chen, Steve Rust, Yungui Huang, Rajiv Ramnath
In this work, we develop a novel, completely unsupervised, neural language model-based document ranking approach to semantic tagging of documents, using the document to be tagged as a query into the GLM to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text.