Ken Chen
Shanghai Jiao Tong University
7 Papers
21 Citations
Ken Chen is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Graph embedding & Semantic similarity. The author has an hindex of 4, co-authored 7 publications.
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Papers
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
Zhenghui Wang,Yanru Qu,Liheng Chen,Jian Shen,Weinan Zhang,Shaodian Zhang,Yimei Gao,Gen Gu,Ken Chen,Yong Yu +9 more
- 24 Apr 2018
TL;DR: A label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts, is proposed.
Development of a Consumer Health Vocabulary by Mining Health Forum Texts Based on Word Embedding: Semiautomatic Approach
Gen Gu,Xingting Zhang,Xingeng Zhu,Zhe Jian,Ken Chen,Dong Wen,Li Gao,Shaodian Zhang,Fei Wang,Handong Ma,Jianbo Lei +10 more
TL;DR: By integrating a large amount of text information and existing consumer health vocabularies, the method outperformed several baseline ranking methods and is effective for generating a list of candidate terms for human review during consumer health vocabulary development.
18
Sevoflurane-induced memory impairment in the postnatal developing mouse brain
TL;DR: The results of the current study suggest that caspase-3 induced cleavage of PARP, as well as pro-BDNF, p75NTR and PKB/Akt may be important in sevoflurane-induced memory impairment in the postnatal developing mouse brain.
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
Liheng Chen,Yanru Qu,Zhenghui Wang,Lin Qiu,Weinan Zhang,Ken Chen,Shaodian Zhang,Yong Yu +7 more
- 13 May 2019
TL;DR: Wang et al. as discussed by the authors proposed Text-driven Graph Embedding with Pairs Sampling (TGE-PS), which uses pairs sampling to improve the sampling strategy of random walk-based embedding models.
1
Alpha‐2 agonists for long‐term sedation during mechanical ventilation in critically ill patients
TL;DR: The safety and efficacy of alpha-2 agonists for sedation of more than 24 hours, compared with traditional sedatives, in mechanically-ventilated critically ill patients is assessed, with high levels of heterogeneity in risk of delirium.