Machine learning can identify newly diagnosed patients with CLL at high risk of infection

Rudi Agius(Copenhagen University Hospital), Carsten Utoft Niemann(Copenhagen University Hospital), Michael Asger Andersen(Copenhagen University Hospital), Jacob Bergstedt(Institut Pasteur), Carmen Herling(University of Cologne), Christian Brieghel(Copenhagen University Hospital), Alexander T. Pearson(University of Chicago), Jakob Hjorth von Stemann(Copenhagen University Hospital), Cameron Ross MacPherson(Copenhagen University Hospital), Alessandro Cozzi‐Lepri(University College London), Jan Larsen(Technical University of Denmark), Jasmin Bahlo(University Hospital Cologne), Michael Hallek(University of Cologne), Jens Lundgren(Hvidovre Hospital), Christen Lykkegaard Andersen(Copenhagen University Hospital), Yoram Louzoun(Bar-Ilan University), Bruno Ledergerber(University of Zurich), Man‐Hung Eric Tang(Copenhagen University Hospital), Magnus Fontes(Institut Pasteur), Mette Jørgensen(Copenhagen University Hospital)
Nature Communications
January 17, 2020
10.1038/s41467-019-14225-8
Cited by 125

Related Papers

Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia updating the National Cancer Institute–Working Group 1996 guidelines
|Blood|2008|3.2k
iwCLL guidelines for diagnosis, indications for treatment, response assessment, and supportive management of CLL
|Blood|2018|1.7k
Idelalisib and Rituximab in Relapsed Chronic Lymphocytic Leukemia
|New England Journal of Medicine|2014|1.7k
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
|Nature Medicine|2019|1.4k
Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers
|npj Digital Medicine|2023|651