![]() ![]() Natural language processing (NLP) techniques have been steadily evolving over the past years and offer several opportunities for retrieving information traditionally trapped by narrative language in electronic medical records ( 1). ![]() From the Clinic for Diagnostic and Interventional Radiology (M.A.F., A.B., M.M., M.S., M.K., C.P.H., H.U.K., T.F.W.) and Pattern Analysis and Learning Group, Department of Radiation Oncology (K.M.H.), Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany (M.A.F., A.B., M.M., M.S., M.K., C.P.H., H.U.K., T.F.W.) Faculty of Mathematics and Computer Science (K.K.) and Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Heidelberg Thoracic Clinic (C.P.H.), Heidelberg University, Heidelberg, Germany Division of Medical Image Computing (K.K., G.K., K.M.H.), Department of Computer Assisted Medical Interventions (CAMI) (J.S.), and Department of Radiology (H.P.S.), German Cancer Research Center (DKFZ), Heidelberg, Germany German Cancer Consortium (DKTK), Partner Sites Essen and Heidelberg, Heidelberg, Germany (H.P.S., K.M.H., J.K.) and Institute for Artificial Intelligence in Medicine (IKIM), University Medicine Essen, Essen, Germany (J.K.). ![]()
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