Multiple features for clinical relation extraction: A machine learning approach-Reference-Cited by-同舟云学术

Multiple features for clinical relation extraction: A machine learning approach

Published:2020-03 Issue: Volume:103 Page:103382
ISSN:1532-0464
Container-title:Journal of Biomedical Informatics
language:en
Short-container-title:Journal of Biomedical Informatics

Author:

Alimova Ilseyar,Tutubalina Elena^ORCID

Funder

Russian Foundation for Basic Research

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference45 articles.

1. Community challenges in biomedical text mining over 10 years: success, failure and the future;Huang;Briefings Bioinformat.,2015

2. Methods for linking ehr notes to education materials;Zheng;Informat. Retrieval J.,2016

3. Loctext: relation extraction of protein localizations to assist database curation;Cejuela;BMC Bioinformat.,2018

4. Y. Zhang, Z. Lu, Exploring semi-supervised variational autoencoders for biomedical relation extraction, Methods (2019).

5. D. Ningthoujam, S. Yadav, P. Bhattacharyya, A. Ekbal, Relation extraction between the clinical entities based on the shortest dependency path based lstm, arXiv preprint arXiv:1903.09941.

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transformers and large language models in healthcare: A review;Artificial Intelligence in Medicine;2024-08

2. Large Language Models and Genomics for Summarizing the Role of microRNA in Regulating mRNA Expression;Biomedicines;2024-07-10

3. Developing a natural language processing system using transformer-based models for adverse drug event detection in electronic health records;2024-07-10

4. Deep Learning-Based Relation Extraction Model for Chinese Medical Case in 6G Cyber Physical Model;Wireless Personal Communications;2024-05-06

5. CAN DEEP LEARNING LARGE LANGUAGE MODELS BE USED TO UNRAVEL KNOWLEDGE GRAPH CREATION?;International Conference on Computing, Machine Learning and Data Science;2024-04-12