Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …

The web is being loaded daily with a huge volume of data, mainly unstructured textual data,
which increases the need for information extraction and NLP systems significantly. Named …

Objectives Biomedical natural language processing tools are increasingly being applied for
broad-coverage information extraction—extracting medical information of all types in a …

知识图谱是赋予机器背景知识的大规模语义网络. 利用知识图谱对多源异构的医学信息进行有序
化组织, 能有效提升海量医学资源的利用价值, 推动医学智能化发展. 从知识图谱的关键技术 …

Electronic health records (EHRs) contain rich documentation regarding disease symptoms
and progression, but EHR data is challenging to use for diagnosis prediction due to its high …

Objectives A substantial portion of the data contained in Electronic Health Records (EHR) is
unstructured, often appearing as free text. This format restricts its potential utility in clinical …

Linking clinical narratives to standardized vocabularies and coding systems is a key
component of unlocking the information in medical text for analysis. However, many …

Named entity recognition (NER) can be a challenging task, especially in highly inflected
languages where each entity can have many different surface forms. We have created the …

Machine learning has demonstrated superior performance in solving many problems in
various fields of medicine compared to non-machine learning approaches. The aim of this …

The data generated by the structured electronic medical records is helpful for mining and
extracting medical data, and it is an effective way to make effective use of valuable data …