382 citations · 563 across the 24 of their papers we have counts for
7 papers · 1 filter
Exploring Large Language Models for Knowledge Graph Completion
Liang Yao, Jiazhen Peng, Chengsheng Mao +1
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Langua…
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Chengsheng Mao, Jie Xu, Luke Rasmussen +9
Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from…
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
Chengsheng Mao, Liang Yao, Yuan Luo
Acute kidney injury (AKI) is a common clinical syndrome characterized by a sudden episode of kidney failure or kidney damage within a few hours or a few days. Accurate early predic…
KG-BERT: BERT for Knowledge Graph Completion
Liang Yao, Chengsheng Mao, Yuan Luo
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models fo…
Graph Convolutional Networks for Text Classification
Liang Yao, Chengsheng Mao, Yuan Luo
Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolutio…
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
Liang Yao, Chengsheng Mao, Yuan Luo
Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based featur…
Developing a Portable Natural Language Processing Based Phenotyping System
Himanshu Sharma, Chengsheng Mao, Yizhen Zhang +7
This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches. Our system utilizes UMLS to extr…