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1 Ergebnisse
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Machine learning models accurately interpret liver histolog..:
Travis, Nate
;
Billaut, Vincent
;
Pokkalla, Harsha
...
Journal of Hepatology. 73 (2020) - p. S485-S486 , 2020
Link:
https://doi.org/10.1016/s0168-8278(20)31451-3
RT Journal T1
Machine learning models accurately interpret liver histology and are associated with disease progression in patients with primary sclerosing cholangitis
UL https://suche.suub.uni-bremen.de/peid=cr-10.1016_s0168-8278(20)31451-3&Exemplar=1&LAN=DE A1 Travis, Nate A1 Billaut, Vincent A1 Pokkalla, Harsha A1 Pethia, Kishalve A1 Zevallos, Oscar A1 Glass, Benjamin A1 Taylor, Amaro A1 Bowlus, Christopher A1 Tanaka, Atsushi A1 Thorburn, Douglas A1 Lu, Xiaomin A1 Huss, Ryan A1 Chung, Chuhan A1 Subramanian, Mani A1 Myers, Robert A1 Muir, Andrew A1 Kowdley, Kris V. A1 Goodman, Zachary A1 Khosla, Aditya A1 Beck, Andrew A1 Resnick, Murray A1 Wapinski, Ilan A1 Trauner, Michael A1 Levy, Cynthia PB Elsevier BV YR 2020 SN 0168-8278 JF Journal of Hepatology VO 73 SP S485 OP S486 LK http://dx.doi.org/https://doi.org/10.1016/s0168-8278(20)31451-3 DO https://doi.org/10.1016/s0168-8278(20)31451-3 SF ELIB - SuUB Bremen
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