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Entity recognition from clinical texts via recurrent neural network
TLDR
This paper comprehensively investigates the performance of LSTM (long-short term memory), a representative variant of RNN, on clinical entity recognition and protected health information recognition, and shows that L STM outperforms traditional machine learning methods that suffer from fussy feature engineering.
An automatic system to identify heart disease risk factors in clinical texts over time
HITSZ _ CNER : A hybrid system for entity recognition from Chinese clinical text
TLDR
A hybrid system based on rule, CRF (conditional random fields) and RNN (recurrent neural network) methods for the CNER task, ranking first in the 2017 CCKS CNER challenge.
A Chinese question answering system based on web search
TLDR
A Chinese question answering system which uses the real-time network information retrieved by search engines to extract the answers by inputting a natural language question.
CMedTEX: A Rule-based Temporal Expression Extraction and Normalization System for Chinese Clinical Notes
TLDR
Compared with HeidelTime for Chinese newswire text, the system developed by this study is much better, indicating that it is necessary to develop a specific TE extraction and normalization system for Chinese clinical notes because of domain difference.
A Mixed Deterministic Model for Coreference Resolution
TLDR
A mixed deterministic model for coreference resolution in the CoNLL-2012 shared task is presented and several sub-tasks are solved by machine learning method and deterministic rules based on multi-filters, such as lexical, syntactic, semantic, gender and number information.
Chinese Clinical Entity Recognition via Attention-Based CNN-LSTM-CRF
TLDR
Experimental results show that the proposed neural network outperforms CRF and LSTM-CRF, and is compared with other two state-of-the-art methods on two benchmark datasets.
Temporal indexing of medical entity in Chinese clinical notes
TLDR
A recurrent convolutional neural network (RNN-CNN) model for the temporal indexing task, which can capture more semantic information from the context of medical entities and temporal expressions, and performs much better than the traditional rule-based and SVM-based method.
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