[PDF][PDF] 电子病历命名实体识别和实体关系抽取研究综述
杨锦锋， 于秋滨， 关毅， 蒋志鹏 - 自动化学报, 2014 - researchgate.net
摘要电子病历(Electronic medical records, EMR) 产生于临床治疗过程, 其中命名实体和实体
关系反映了患者健康状况, 包含了大量与患者健康状况密切相关的医疗知识 …
关系反映了患者健康状况, 包含了大量与患者健康状况密切相关的医疗知识 …
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Abstract The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for
Clinical Records presented three tasks: a concept extraction task focused on the extraction …
Clinical Records presented three tasks: a concept extraction task focused on the extraction …
[HTML][HTML] Recurrent neural networks for classifying relations in clinical notes
Y Luo - Journal of biomedical informatics, 2017 - Elsevier
We proposed the first models based on recurrent neural networks (more specifically Long
Short-Term Memory-LSTM) for classifying relations from clinical notes. We tested our models …
Short-Term Memory-LSTM) for classifying relations from clinical notes. We tested our models …
[HTML][HTML] Multiple features for clinical relation extraction: A machine learning approach
I Alimova, E Tutubalina - Journal of biomedical informatics, 2020 - Elsevier
Relation extraction aims to discover relational facts about entity mentions from plain texts. In
this work, we focus on clinical relation extraction; namely, given a medical record with …
this work, we focus on clinical relation extraction; namely, given a medical record with …
Relation extraction from biomedical and clinical text: Unified multitask learning framework
Motivation: To minimize the accelerating amount of time invested on the biomedical
literature search, numerous approaches for automated knowledge extraction have been …
literature search, numerous approaches for automated knowledge extraction have been …
Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes
Abstract We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying
relations from clinical notes. Seg-CNNs use only word-embedding features without manual …
relations from clinical notes. Seg-CNNs use only word-embedding features without manual …
Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs)
We propose to use segment graph convolutional and recurrent neural networks (Seg-
GCRNs), which use only word embedding and sentence syntactic dependencies, to classify …
GCRNs), which use only word embedding and sentence syntactic dependencies, to classify …
Trustworthy assertion classification through prompting
Accurate identification of the presence, absence or possibility of relevant entities in clinical
notes is important for healthcare professionals to quickly understand crucial clinical …
notes is important for healthcare professionals to quickly understand crucial clinical …
Integrating text embedding with traditional nlp features for clinical relation extraction
Recently, text embedding techniques such as Word2Vec and BERT have produced state-of-
the-art results in a wide variety of NLP tasks. As a result, traditional NLP features frequently …
the-art results in a wide variety of NLP tasks. As a result, traditional NLP features frequently …
Recurrent neural networks with segment attention and entity description for relation extraction from clinical texts
Z Li, J Yang, X Gou, X Qi - Artificial intelligence in medicine, 2019 - Elsevier
At present, great progress has been achieved on the relation extraction for clinical texts, but
we have noticed that the current models have great drawbacks when dealing with long …
we have noticed that the current models have great drawbacks when dealing with long …