Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

Current models for correlating electronic medical records with-omics data largely ignore
clinical text, which is an important source of phenotype information for patients with cancer …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

We propose a joint event and temporal relation extraction model with shared representation
learning and structured prediction. The proposed method has two advantages over existing …

Understanding events entails recognizing the structural and temporal orders between event
mentions to build event structures/graphs for input documents. To achieve this goal, our …

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

A critical part of reading is being able to understand the temporal relationships between
events described in a passage of text, even when those relationships are not explicitly …

Health knowledge graph provides an ideal technical means to integrate heterogeneous data
resources and enhance knowledge-based services. There are many challenges for the …

Classic methods for clinical temporal relation extraction focus on relational candidates within
a sentence. On the other hand, break-through Bidirectional Encoder Representations from …

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality
improvement, research, and operations. However, much of the most valuable information …