US 12,002,582 B2
Method for obtaining disease-related clinical information
Alexander Muehlberg, Nuremberg (DE); Oliver Taubmann, Weilersbach (DE); Alexander Katzmann, Fuerth (DE); and Michael Suehling, Erlangen (DE)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by Siemens Healthcare GmbH, Erlangen (DE)
Filed on Dec. 2, 2020, as Appl. No. 17/109,332.
Claims priority of application No. 19215716 (EP), filed on Dec. 12, 2019.
Prior Publication US 2021/0183514 A1, Jun. 17, 2021
Int. Cl. G16H 50/20 (2018.01); A61B 6/00 (2006.01); A61B 6/03 (2006.01); G06T 7/00 (2017.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01)
CPC G16H 50/20 (2018.01) [A61B 6/032 (2013.01); A61B 6/5217 (2013.01); G06T 7/0016 (2013.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30096 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method for providing clinical information, the computer-implemented method comprising:
receiving input data, the input data including a graph representation of a plurality of disease lesions of a patient, the graph representation including a plurality of nodes and a plurality of edges connecting the plurality of nodes, the plurality of nodes encoding the plurality of disease lesions;
applying a trained function to the input data to generate the clinical information, the trained function being based on a graph machine learning model; and
providing the clinical information, the clinical information including information for prediction of at least one of disease progression, survival, or therapy response of the patient.