1 code implementation • NAACL (NLPMC) 2021 • Betty van Aken, Ivana Trajanovska, Amy Siu, Manuel Mayrdorfer, Klemens Budde, Alexander Loeser
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.
1 code implementation • LREC 2022 • Roland Roller, Aljoscha Burchardt, Nils Feldhus, Laura Seiffe, Klemens Budde, Simon Ronicke, Bilgin Osmanodja
In recent years, machine learning for clinical decision support has gained more and more attention.
Explainable Artificial Intelligence (XAI)
Feature Importance
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.
no code implementations • 27 Apr 2022 • Roland Roller, Klemens Budde, Aljoscha Burchardt, Peter Dabrock, Sebastian Möller, Bilgin Osmanodja, Simon Ronicke, David Samhammer, Sven Schmeier
Scientific publications about machine learning in healthcare are often about implementing novel methods and boosting the performance - at least from a computer science perspective.
1 code implementation • EACL 2021 • Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix A. Gers, Alexander Löser
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.
Ranked #1 on
Length-of-Stay prediction
on Clinical Admission Notes from MIMIC-III
(using extra training data)
no code implementations • WS 2016 • Rol Roller, , Hans Uszkoreit, Feiyu Xu, Laura Seiffe, Michael Mikhailov, Oliver Staeck, Klemens Budde, Fabian Halleck, Danilo Schmidt
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.
no code implementations • WS 2016 • Viviana Cotik, Rol Roller, , Feiyu Xu, Hans Uszkoreit, Klemens Budde, Danilo Schmidt
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.