Nature Communications (Jan 2020)
Machine learning can identify newly diagnosed patients with CLL at high risk of infection
- Rudi Agius,
- Christian Brieghel,
- Michael A. Andersen,
- Alexander T. Pearson,
- Bruno Ledergerber,
- Alessandro Cozzi-Lepri,
- Yoram Louzoun,
- Christen L. Andersen,
- Jacob Bergstedt,
- Jakob H. von Stemann,
- Mette Jørgensen,
- Man-Hung Eric Tang,
- Magnus Fontes,
- Jasmin Bahlo,
- Carmen D. Herling,
- Michael Hallek,
- Jens Lundgren,
- Cameron Ross MacPherson,
- Jan Larsen,
- Carsten U. Niemann
Affiliations
- Rudi Agius
- Department of Mathematics and Computer Science, Technical University of Denmark
- Christian Brieghel
- Department of Hematology, Rigshospitalet, Copenhagen University Hospital
- Michael A. Andersen
- Department of Hematology, Rigshospitalet, Copenhagen University Hospital
- Alexander T. Pearson
- Department of Medicine, University of Chicago
- Bruno Ledergerber
- University of Zurich
- Alessandro Cozzi-Lepri
- University College London
- Yoram Louzoun
- Department of Mathematics, Bar-Ilan University
- Christen L. Andersen
- Department of Hematology, Rigshospitalet, Copenhagen University Hospital
- Jacob Bergstedt
- Human Evolutionary Genetics Unit, Institut Pasteur
- Jakob H. von Stemann
- Rigshospitalet, Copenhagen University Hospital
- Mette Jørgensen
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital
- Man-Hung Eric Tang
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital
- Magnus Fontes
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital
- Jasmin Bahlo
- Department of Internal Medicine and Center of Integrated Oncology Cologne Bonn, University Hospital
- Carmen D. Herling
- Department of Internal Medicine and Center of Integrated Oncology Cologne Bonn, University Hospital
- Michael Hallek
- Department of Internal Medicine and Center of Integrated Oncology Cologne Bonn, University Hospital
- Jens Lundgren
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital
- Cameron Ross MacPherson
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital
- Jan Larsen
- Department of Mathematics and Computer Science, Technical University of Denmark
- Carsten U. Niemann
- Department of Hematology, Rigshospitalet, Copenhagen University Hospital
- DOI
- https://doi.org/10.1038/s41467-019-14225-8
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 17
Abstract
Chronic lymphocytic leukemia is an indolent disease, and many patients succumb to infection rather than the direct effects of the disease. Here, the authors use medical records and machine learning to predict the patients that may be at risk of infection, which may enable a change in the course of their treatment.
