most citedEMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

2 citations · 4 across the 5 of their papers we have counts for

collaborators

8 papers

cs.AI2018

Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis

Jingchi Jiang, Huanzheng Wang, Jing Xie +3

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic represe…

cs.CL2018

Convolutional Gated Recurrent Units for Medical Relation Classification

Bin He, Yi Guan, Rui Dai

Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for relation classification. We propose a unified architecture, whic…

cs.CL2018

Classifying medical relations in clinical text via convolutional neural networks

Bin He, Yi Guan, Rui Dai

Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a…

cs.AI2017

Classification of entities via their descriptive sentences

Chao Zhao, Min Zhao, Yi Guan

Hypernym identification of open-domain entities is crucial for taxonomy construction as well as many higher-level applications. Current methods suffer from either low precision or…

cs.CL20171 cited

De-identification of medical records using conditional random fields and long short-term memory networks

Zhipeng Jiang, Chao Zhao, Bin He +2

The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records. This paper describes two participatin…

cs.CL2017

Constructing a Hierarchical User Interest Structure based on User Profiles

Chao Zhao, Min Zhao, Yi Guan

The interests of individual internet users fall into a hierarchical structure which is useful in regards to building personalized searches and recommendations. Most studies on this…