Health care professionals produce abundant textual information in their daily clinical
practice and this information is stored in many diverse sources and, generally, in textual …

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 …

Abstract Introduction Artificial Intelligence (AI) is a burning topic and use of AI in our day‐to‐
day life has increased exponentially. The purpose of this study was to evaluate the attitudes …

Informatics for integrating biology and the bedside (i2b2) seeks to provide the
instrumentation for using the informational by-products of health care and the biological …

Relations between medical concepts convey meaningful medical knowledge and patients'
health information. Relation extraction on Clinical texts is an important task of information …

Research of named entity recognition (NER) on electrical medical records (EMRs) focuses
on verifying whether methods to NER in traditional texts are effective for that in EMRs, and …

Abstract We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying
relations from clinical notes. Seg-CNNs use only word-embedding features without manual …

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 …

We propose to use segment graph convolutional and recurrent neural networks (Seg-
GCRNs), which use only word embedding and sentence syntactic dependencies, to classify …

This paper reports on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab which
extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on template …