10 citations · 16 across the 8 of their papers we have counts for
8 papers
Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks
Xianlong Zeng, Simon Lin, Chang Liu
The adoption of electronic health records (EHR) has become universal during the past decade, which has afforded in-depth data-based research. By learning from the large amount of h…
Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis
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 l…
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
Xiang Yue, Xinliang Frederick Zhang, Ziyu Yao +2
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus…
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
Xinliang Frederick Zhang, Heming Sun, Xiang Yue +2
We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set…
Rationalizing Medical Relation Prediction from Corpus-level Statistics
Zhen Wang, Jennifer Lee, Simon Lin +1
Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain. Aiming to shed some light on how to rationalize medi…
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization
Manirupa Das, Juanxi Li, Eric Fosler-Lussier +5
Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or…