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

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 t...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/d34aa0eace4042dc8ea0d505bfcb57d5
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spelling	oai:doaj.org-article:d34aa0eace4042dc8ea0d505bfcb57d52021-12-02T15:39:14ZMachine learning can identify newly diagnosed patients with CLL at high risk of infection10.1038/s41467-019-14225-82041-1723https://doaj.org/article/d34aa0eace4042dc8ea0d505bfcb57d52020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-14225-8https://doaj.org/toc/2041-1723Chronic 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.Rudi AgiusChristian BrieghelMichael A. AndersenAlexander T. PearsonBruno LedergerberAlessandro Cozzi-LepriYoram LouzounChristen L. AndersenJacob BergstedtJakob H. von StemannMette JørgensenMan-Hung Eric TangMagnus FontesJasmin BahloCarmen D. HerlingMichael HallekJens LundgrenCameron Ross MacPhersonJan LarsenCarsten U. NiemannNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-17 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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 Machine learning can identify newly diagnosed patients with CLL at high risk of infection
description	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.
format	article
author	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
author_facet	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
author_sort	Rudi Agius
title	Machine learning can identify newly diagnosed patients with CLL at high risk of infection
title_short	Machine learning can identify newly diagnosed patients with CLL at high risk of infection
title_full	Machine learning can identify newly diagnosed patients with CLL at high risk of infection
title_fullStr	Machine learning can identify newly diagnosed patients with CLL at high risk of infection
title_full_unstemmed	Machine learning can identify newly diagnosed patients with CLL at high risk of infection
title_sort	machine learning can identify newly diagnosed patients with cll at high risk of infection
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/d34aa0eace4042dc8ea0d505bfcb57d5
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Machine learning can identify newly diagnosed patients with CLL at high risk of infection

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