62 citations · 285 across the 35 of their papers we have counts for
6 papers · 1 filter
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Denis Jered McInerney, Luyang Kong, Kristjan Arumae +2
Multi-task learning is useful in NLP because it is often practically desirable to have a single model that works across a range of tasks. In the medical domain, sequential training…
Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
Xiaokai Wei, Shen Wang, Dejiao Zhang +2
Pretrained Language Models (PLM) have established a new paradigm through learning informative contextualized representations on large-scale text corpus. This new paradigm has revol…
Improving Early Sepsis Prediction with Multi Modal Learning
Fred Qin, Vivek Madan, Ujjwal Ratan +4
Sepsis is a life-threatening disease with high morbidity, mortality and healthcare costs. The early prediction and administration of antibiotics and intravenous fluids is considere…
Neural Entity Recognition with Gazetteer based Fusion
Qing Sun, Parminder Bhatia
Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain. In this paper, we focus on clinical domain where only li…
Zero-shot Medical Entity Retrieval without Annotation: Learning From Rich Knowledge Graph Semantics
Luyang Kong, Christopher Winestock, Parminder Bhatia
Medical entity retrieval is an integral component for understanding and communicating information across various health systems. Current approaches tend to work well on specific me…
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani +4
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the tas…