In recent years, machine learning for clinical decision support has gained more and more attention.
In order to provide high-quality care, health professionals must efficiently identify the presence, possibility, or absence of symptoms, treatments and other relevant entities in free-text clinical notes.
no code implementations • 8 Jul 2022 • Roland Roller, Laura Seiffe, Ammer Ayach, Sebastian Möller, Oliver Marten, Michael Mikhailov, Christoph Alt, Danilo Schmidt, Fabian Halleck, Marcel Naik, Wiebke Duettmann, Klemens Budde
However, in the context of clinical text processing the number of accessible datasets is scarce -- and so is the number of existing tools.
Scientific publications about machine learning in healthcare are often about implementing novel methods and boosting the performance - at least from a computer science perspective.
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.
Ranked #1 on Medical Diagnosis on Clinical Admission Notes from MIMIC-III (using extra training data)
In this work we present a fine-grained annotation schema to detect named entities in German clinical data of chronically ill patients with kidney diseases.
This work presents a system for detecting mentions of clinical findings that are negated or just speculated.
no code implementations • 17 Nov 2013 • Volker Tresp, Sonja Zillner, Maria J. Costa, Yi Huang, Alexander Cavallaro, Peter A. Fasching, Andre Reis, Martin Sedlmayr, Thomas Ganslandt, Klemens Budde, Carl Hinrichs, Danilo Schmidt, Philipp Daumke, Daniel Sonntag, Thomas Wittenberg, Patricia G. Oppelt, Denis Krompass
We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i. e., with data from many patients and with complete patient information.