Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Grundmann, Paul"'
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:
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
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
We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structured query tuples of entities and aspects from free text and medical ta
Externí odkaz:
http://arxiv.org/abs/2002.00835
Autor:
Schneider, Rudolf, Oberhauser, Tom, Grundmann, Paul, Gers, Felix Alexander, Löser, Alexander, Staab, Steffen
Universal embeddings, such as BERT or ELMo, are useful for a broad set of natural language processing tasks like text classification or sentiment analysis. Moreover, specialized embeddings also exist for tasks like topic modeling or named entity disa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______348::8aeac89a21761f07f453b59f61f25d81
https://eprints.soton.ac.uk/438613/
https://eprints.soton.ac.uk/438613/