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pro vyhledávání: '"Kennedy, Paul A."'
Motivation: Biomedical named-entity normalization involves connecting biomedical entities with distinct database identifiers in order to facilitate data integration across various fields of biology. Existing systems for biomedical named entity normal
Externí odkaz:
http://arxiv.org/abs/2310.14366
Autor:
Wilkins-Caruana, Adrian, Bandara, Madhushi, Musial, Katarzyna, Catchpoole, Daniel, Kennedy, Paul J.
Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision p
Externí odkaz:
http://arxiv.org/abs/2309.01897
Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learni
Externí odkaz:
http://arxiv.org/abs/2308.14216
A meaningful understanding of clinical protocols and patient pathways helps improve healthcare outcomes. Electronic health records (EHR) reflect real-world treatment behaviours that are used to enhance healthcare management but present challenges; pr
Externí odkaz:
http://arxiv.org/abs/2110.01160
Autor:
Kennedy, Paul, author
Publikováno v:
The Future of Ocean Governance and Capacity Development: Essays in Honor of Elisabeth Mann Borgese (1918–2002). :503-506
Autor:
Kennedy, Paul T., Saulters, Emma L., Duckworth, Andrew D., Lim, Yeong Jer, Woolley, John F., Slupsky, Joseph R., Cragg, Mark S., Ward, Frank J., Dahal, Lekh N.
Publikováno v:
In Molecular Therapy 7 February 2024 32(2):457-468
Akademický článek
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Autor:
Power, Tamara, Kennedy, Paul, Chen, Hui, Martinez-Maldonado, Roberto, McGregor, Carolyn, Johnson, Anna, Townsend, Lisa, Hayes, Carolyn
Publikováno v:
In Clinical Simulation in Nursing April 2023 77:23-29
Publikováno v:
2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 364-371
Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to co
Externí odkaz:
http://arxiv.org/abs/1802.03145