Named entity recognition and relation detection for biomedical information extraction

N Perera, M Dehmer, F Emmert-Streib - Frontiers in cell and …, 2020 - frontiersin.org
The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives

J Xu, P Yang, S Xue, B Sharma, M Sanchez-Martin… - Human genetics, 2019 - Springer
In the field of cancer genomics, the broad availability of genetic information offered by next-
generation sequencing technologies and rapid growth in biomedical publication has led to …

Radgraph: Extracting clinical entities and relations from radiology reports

S Jain, A Agrawal, A Saporta, SQH Truong… - ar** the relationship between information technology and mental healthcare
T Timakum, Q **e, M Song - BMC psychiatry, 2022 - Springer
Background E-mental healthcare is the convergence of digital technologies with mental
health services. It has been developed to fill a gap in healthcare for people who need mental …

A bidirectional LSTM and conditional random fields approach to medical named entity recognition

K Xu, Z Zhou, T Hao, W Liu - … of the International Conference on Advanced …, 2018 - Springer
Medical named entity recognition is a fundamental and essential research for medical
natural language possessing, aiming to identifying medical concepts or terminology such as …

Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype

KM Malik, M Krishnamurthy, M Alobaidi… - Expert Systems with …, 2020 - Elsevier
To derive meaningful insights from voluminous healthcare data, it is essential to convert it
into machine understandable knowledge. Currently, machine understandable domain …