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Machine learning can identify newly diagnosed patients with CLL at high risk of infection. / Agius, Rudi; Brieghel, Christian; Andersen, Michael A.; Pearson, Alexander T.; Ledergerber, Bruno; Cozzi-Lepri, Alessandro; Louzoun, Yoram; Andersen, Christen L.; Bergstedt, Jacob; von Stemann, Jakob H.; Jørgensen, Mette; Tang, Man-Hung Eric; Fontes, Magnus; Bahlo, Jasmin; Herling, Carmen D.; Hallek, Michael; Lundgren, Jens; MacPherson, Cameron Ross; Larsen, Jan; Niemann, Carsten U.
I:
Nature Communications, Bind 11, Nr. 1, 363, 2020.
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
Agius, R, Brieghel, C, Andersen, MA, Pearson, AT, Ledergerber, B, Cozzi-Lepri, A, Louzoun, Y
, Andersen, CL, Bergstedt, J, von Stemann, JH, Jørgensen, M, Tang, M-HE, Fontes, M, Bahlo, J, Herling, CD, Hallek, M
, Lundgren, J, MacPherson, CR, Larsen, J
& Niemann, CU 2020, '
Machine learning can identify newly diagnosed patients with CLL at high risk of infection',
Nature Communications, bind 11, nr. 1, 363.
https://doi.org/10.1038/s41467-019-14225-8APA
Agius, R., Brieghel, C., Andersen, M. A., Pearson, A. T., Ledergerber, B., Cozzi-Lepri, A., Louzoun, Y.
, Andersen, C. L., Bergstedt, J., von Stemann, J. H., Jørgensen, M., Tang, M-H. E., Fontes, M., Bahlo, J., Herling, C. D., Hallek, M.
, Lundgren, J., MacPherson, C. R., Larsen, J.
, & Niemann, C. U. (2020).
Machine learning can identify newly diagnosed patients with CLL at high risk of infection.
Nature Communications,
11(1), [363].
https://doi.org/10.1038/s41467-019-14225-8Vancouver
Agius R, Brieghel C, Andersen MA, Pearson AT, Ledergerber B, Cozzi-Lepri A o.a.
Machine learning can identify newly diagnosed patients with CLL at high risk of infection.
Nature Communications. 2020;11(1). 363.
https://doi.org/10.1038/s41467-019-14225-8Author
Agius, Rudi ; Brieghel, Christian ; Andersen, Michael A. ; Pearson, Alexander T. ; Ledergerber, Bruno ; Cozzi-Lepri, Alessandro ; Louzoun, Yoram ; Andersen, Christen L. ; Bergstedt, Jacob ; von Stemann, Jakob H. ; Jørgensen, Mette ; Tang, Man-Hung Eric ; Fontes, Magnus ; Bahlo, Jasmin ; Herling, Carmen D. ; Hallek, Michael ; Lundgren, Jens ; MacPherson, Cameron Ross ; Larsen, Jan ; Niemann, Carsten U. / Machine learning can identify newly diagnosed patients with CLL at high risk of infection. I: Nature Communications. 2020 ; Bind 11, Nr. 1.
Bibtex
@article{3a0c7cceb22f4aa3bc086daaf00db261,
title = "Machine learning can identify newly diagnosed patients with CLL at high risk of infection",
author = "Rudi Agius and Christian Brieghel and Andersen, {Michael A.} and Pearson, {Alexander T.} and Bruno Ledergerber and Alessandro Cozzi-Lepri and Yoram Louzoun and Andersen, {Christen L.} and Jacob Bergstedt and {von Stemann}, {Jakob H.} and Mette J{\o}rgensen and Tang, {Man-Hung Eric} and Magnus Fontes and Jasmin Bahlo and Herling, {Carmen D.} and Michael Hallek and Jens Lundgren and MacPherson, {Cameron Ross} and Jan Larsen and Niemann, {Carsten U.}",
year = "2020",
doi = "10.1038/s41467-019-14225-8",
language = "English",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",
number = "1",
}
RIS
TY - JOUR
T1 - Machine learning can identify newly diagnosed patients with CLL at high risk of infection
AU - Agius, Rudi
AU - Brieghel, Christian
AU - Andersen, Michael A.
AU - Pearson, Alexander T.
AU - Ledergerber, Bruno
AU - Cozzi-Lepri, Alessandro
AU - Louzoun, Yoram
AU - Andersen, Christen L.
AU - Bergstedt, Jacob
AU - von Stemann, Jakob H.
AU - Jørgensen, Mette
AU - Tang, Man-Hung Eric
AU - Fontes, Magnus
AU - Bahlo, Jasmin
AU - Herling, Carmen D.
AU - Hallek, Michael
AU - Lundgren, Jens
AU - MacPherson, Cameron Ross
AU - Larsen, Jan
AU - Niemann, Carsten U.
PY - 2020
Y1 - 2020
U2 - 10.1038/s41467-019-14225-8
DO - 10.1038/s41467-019-14225-8
M3 - Journal article
C2 - 31953409
VL - 11
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 363
ER -
