no code implementations • LREC 2022 • Laura Seiffe, Fares Kallel, Sebastian Möller, Babak Naderi, Roland Roller
In order to provide suitable text for the target audience, it is necessary to measure its complexity.
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 • 23 May 2020 • Laura Seiffe, Oliver Marten, Michael Mikhailov, Sven Schmeier, Sebastian Möller, Roland Roller
This also applies to information about a person's health status.
no code implementations • LREC 2020 • Laura Seiffe, Oliver Marten, Michael Mikhailov, Sven Schmeier, Sebastian M{\"o}ller, Rol Roller,
To detect and use those relevant information, laymen language has to be translated and/or linked against the corresponding medical concept.
3 code implementations • CONLL 2017 • Dirk Weissenborn, Georg Wiese, Laura Seiffe
We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.
Ranked #13 on
Question Answering
on NewsQA
(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.