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20182023
most citedExploring Large Language Models for Knowledge Graph Completion

24 citations · 36 across the 3 of their papers we have counts for

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Showing 2018Show all

6 papers · 1 filter

cs.LG2018

Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes

Yikuan Li, Liang Yao, Chengsheng Mao +3

Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will…

cs.LG2018

Distribution Networks for Open Set Learning

Chengsheng Mao, Liang Yao, Yuan Luo

In open set learning, a model must be able to generalize to novel classes when it encounters a sample that does not belong to any of the classes it has seen before. Open set learni…

cs.CV2018

Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images

Chengsheng Mao, Yiheng Pan, Zexian Zeng +2

Thoracic diseases are very serious health problems that plague a large number of people. Chest X-ray is currently one of the most popular methods to diagnose thoracic diseases, pla…

cs.CL2018

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…

cs.CL2018

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…

cs.CL2018

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…