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

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

Abstract 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-serie...

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Bibliographic Details
Main Authors: Malte Jacobsen, Rahil Gholamipoor, Till A. Dembek, Pauline Rottmann, Marlo Verket, Julia Brandts, Paul Jäger, Ben-Niklas Baermann, Mustafa Kondakci, Lutz Heinemann, Anna L. Gerke, Nikolaus Marx, Dirk Müller-Wieland, Kathrin Möllenhoff, Melchior Seyfarth, Markus Kollmann, Guido Kobbe
Format: Article
Language:English
Published: Nature Portfolio 2023-06-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-023-00847-2

Internet

https://doi.org/10.1038/s41746-023-00847-2

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