Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies

Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data re...

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Detalles Bibliográficos
Autores principales: Jacobsen, Malte, Gholamipoor, Rahil, Dembek, Till A., Rottmann, Pauline, Verket, Marlo, Brandts, Julia, Jäger, Paul, Baermann, Ben-Niklas, Kondakci, Mustafa, Heinemann, Lutz, Gerke, Anna L., Marx, Nikolaus, Müller-Wieland, Dirk, Möllenhoff, Kathrin, Seyfarth, Melchior, Kollmann, Markus, Kobbe, Guido
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238496/
https://www.ncbi.nlm.nih.gov/pubmed/37268734
http://dx.doi.org/10.1038/s41746-023-00847-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238496/
https://www.ncbi.nlm.nih.gov/pubmed/37268734
http://dx.doi.org/10.1038/s41746-023-00847-2

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