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arXiv Machine Learning By Jannik L\"ubberstedt, Krischan Braitsch, Jacqueline Lammert, Christof Winter, Florian Gabriel, Tristan Lemke, Christopher Zirn, Markus Graf, Friedrich Puttkammer, Hartmut H\"antze, Johannes Moll, Anirudh Narayanan, Andrei Zhukov, Fabian Drexel, Zeineb Ben Chaaben, Sebastian Ziegelmayer, Su Hwan Kim, Marion H\"ogner, Jan Kirschke, Florian Bassermann, Marcus Makowski, Christian Wachinger, Lisa Adams, Keno Bressem

Routine laboratory trajectories encode the onset of organ-level complications in cancer

Read the original on arXiv Machine Learning →

arXiv:2606. 08538v1 Announce Type: new Abstract: Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-timepoint prognostic tools.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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