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of 82
pro vyhledávání: '"Löser, Alexander"'
Autor:
Arnold, Sebastian, Schneider, Rudolf, Cudré-Mauroux, Philippe, Gers, Felix A., Löser, Alexander
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 7, Pp 169-184 (2019)
When searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates the identification of
Externí odkaz:
https://doaj.org/article/5187928a2c8a46caab4598208ad7488f
Autor:
Adams, Lisa, Busch, Felix, Han, Tianyu, Excoffier, Jean-Baptiste, Ortala, Matthieu, Löser, Alexander, Aerts, Hugo JWL., Kather, Jakob Nikolas, Truhn, Daniel, Bressem, Keno
Background: Recent advancements in large language models (LLMs) offer potential benefits in healthcare, particularly in processing extensive patient records. However, existing benchmarks do not fully assess LLMs' capability in handling real-world, le
Externí odkaz:
http://arxiv.org/abs/2401.14490
Autor:
Han, Tianyu, Adams, Lisa C., Papaioannou, Jens-Michalis, Grundmann, Paul, Oberhauser, Tom, Löser, Alexander, Truhn, Daniel, Bressem, Keno K.
As large language models (LLMs) like OpenAI's GPT series continue to make strides, we witness the emergence of artificial intelligence applications in an ever-expanding range of fields. In medicine, these LLMs hold considerable promise for improving
Externí odkaz:
http://arxiv.org/abs/2304.08247
Autor:
Bressem, Keno K., Papaioannou, Jens-Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo JWL., Löser, Alexander
Publikováno v:
Expert Systems with Applications 2024;237(21):121598
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-ar
Externí odkaz:
http://arxiv.org/abs/2303.08179
Autor:
van Aken, Betty, Papaioannou, Jens-Michalis, Naik, Marcel G., Eleftheriadis, Georgios, Nejdl, Wolfgang, Gers, Felix A., Löser, Alexander
The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce Proto
Externí odkaz:
http://arxiv.org/abs/2210.08500
Autor:
Papaioannou, Jens-Michalis, Grundmann, Paul, van Aken, Betty, Samaras, Athanasios, Kyparissidis, Ilias, Giannakoulas, George, Gers, Felix, Löser, Alexander
Publikováno v:
Proceedings of the Language Resources and Evaluation Conference. 2022; 900-909
Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-of-the-art models are mostly applicable to clinical notes written in E
Externí odkaz:
http://arxiv.org/abs/2208.01912
Decision support systems based on clinical notes have the potential to improve patient care by pointing doctors towards overseen risks. Predicting a patient's outcome is an essential part of such systems, for which the use of deep neural networks has
Externí odkaz:
http://arxiv.org/abs/2111.15512
Retrieving answer passages from long documents is a complex task requiring semantic understanding of both discourse and document context. We approach this challenge specifically in a clinical scenario, where doctors retrieve cohorts of patients based
Externí odkaz:
http://arxiv.org/abs/2108.00775
Autor:
Bressem, Keno K., Papaioannou, Jens-Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo J.W.L., Löser, Alexander
Publikováno v:
In Expert Systems With Applications 1 March 2024 237 Part C
Autor:
Löser, Alexander
Physical data layout is an important performance factor for modern databases. Clustering, i.e., storing similar values in proximity, can lead to performance gains in several ways. We present an automated model to determine beneficial clustering colum
Externí odkaz:
http://arxiv.org/abs/2103.15509