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

Authors :
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
Source :
Nature Communications, Vol 11, Iss 1, Pp 1-17 (2020)
Publication Year :
2020
Publisher :
Nature Portfolio, 2020.

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.34aa0eace4042dc8ea0d505bfcb57d5
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-019-14225-8