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De-identification of medical records using conditional random fields and long short-term memory networks
CRFs based de-identification of medical records
Classifying medical relations in clinical text via convolutional neural networks
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Extraction of risk factors for cardiovascular diseases from Chinese electronic medical records
Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records
- Jia Su, Bin He, Y. Guan, Jingchi Jiang, Jinfeng Yang
- BiologyBMC Medical Informatics and Decision Making
- 28 November 2016
This study builds a corpus of CVD risk factor annotations based on Chinese electronic medical records to develop a risk factor information extraction system that can be applied as a foundation for the further study of the progress of risk factors and CVD.
Convolutional Gated Recurrent Units for Medical Relation Classification
- Bin He, Y. Guan, Rui Dai
- Computer ScienceIEEE International Conference on Bioinformatics…
- 29 July 2018
This work proposes a unified architecture, which exploits the advantages of CNN and RNN simultaneously, to identify medical relations in clinical records, with only word embedding features.
Representing Words as Lymphocytes
Inspired by the analogies between words and lymphocytes, a lymphocyte-style word representation is proposed that is built on the basis of dependency syntax of sentences and represent word context as head properties and dependent properties of the word.
Clinical Named Entity Recognition Method Based on CRF
The experiment shows that the proposed system is effective in the clinical name entity recognition of medical records, achieving a F1 measure of 0.8974 at the strict entity evaluation level which ranked sixth.