Zobrazeno 1 - 10
of 3 122
pro vyhledávání: '"Van Uden"'
Autor:
de Wit, Matthijs, Kleijnen, Mirella, Lissenberg-Witte, Birgit, van Uden-Kraan, Cornelia, Millet, Kobe, Frambach, Ruud, Verdonck-de Leeuw, Irma
Publikováno v:
Journal of Medical Internet Research, Vol 21, Iss 12, p e14985 (2019)
BackgroundSupporting patients to engage in (Web-based) self-management tools is increasingly gaining importance, but the engagement of health care professionals is lagging behind. This can partly be explained by resistance among health care professio
Externí odkaz:
https://doaj.org/article/496ce8effc2f4633bf3ab3231254bac1
Autor:
Van Veen, Dave, Van Uden, Cara, Blankemeier, Louis, Delbrouck, Jean-Benoit, Aali, Asad, Bluethgen, Christian, Pareek, Anuj, Polacin, Malgorzata, Reis, Eduardo Pontes, Seehofnerova, Anna, Rohatgi, Nidhi, Hosamani, Poonam, Collins, William, Ahuja, Neera, Langlotz, Curtis P., Hom, Jason, Gatidis, Sergios, Pauly, John, Chaudhari, Akshay S.
Publikováno v:
Nature Medicine, 2024
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NL
Externí odkaz:
http://arxiv.org/abs/2309.07430
Autor:
Van Uden, Cara, Bluethgen, Christian, Attias, Maayane, Polacin, Malgorzata, Guo, Haiwei Henry, Simha, Neha, Raj, Rishi, Langlotz, Curtis
Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervi
Externí odkaz:
http://arxiv.org/abs/2306.01111
Autor:
Van Uden, Cara, Irvin, Jeremy, Huang, Mars, Dean, Nathan, Carr, Jason, Ng, Andrew, Langlotz, Curtis
Self-supervised learning (SSL) enables label efficient training for machine learning models. This is essential for domains such as medical imaging, where labels are costly and time-consuming to curate. However, the most effective supervised or SSL st
Externí odkaz:
http://arxiv.org/abs/2305.08017
Autor:
Van Veen, Dave, Van Uden, Cara, Attias, Maayane, Pareek, Anuj, Bluethgen, Christian, Polacin, Malgorzata, Chiu, Wah, Delbrouck, Jean-Benoit, Chaves, Juan Manuel Zambrano, Langlotz, Curtis P., Chaudhari, Akshay S., Pauly, John
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, or cli
Externí odkaz:
http://arxiv.org/abs/2305.01146
Publikováno v:
European Journal of Mental Health, Vol 09, Iss 1, Pp 3-19 (2014)
Externí odkaz:
https://doaj.org/article/e2de6ec0a47e4ad8ad12f268fd21eb5e
Autor:
Martinus Franciscus Mohandas van Uden, Johannes Wilhelmus Franciscus Wamelink, Ellen Maria van Bueren, Erwin Wilhelmus Theodurus Martinus Heurkens
Publikováno v:
Cleaner Production Letters, Vol 7, Iss , Pp 100083- (2024)
Researchers employ many different approaches to study transitions towards more sustainable futures, of which Sustainability Transitions Research and Social Practice Theory are often used. These approaches offer complementary concepts that are helpful
Externí odkaz:
https://doaj.org/article/d85c18ad660541cda1afc98b1cc14853
Publikováno v:
European Journal of Mental Health, Vol 07, Iss 1, Pp 57-71 (2012)
Externí odkaz:
https://doaj.org/article/4fafd8b34b1843c7946750d6fada5dbc
Autor:
M.C. Van Maaren, T.A. Hueting, D.J.P. van Uden, M. van Hezewijk, L. de Munck, M.A.M. Mureau, P.A. Seegers, Q.J.M. Voorham, M.K. Schmidt, G.S. Sonke, C.G.M. Groothuis-Oudshoorn, S. Siesling
Publikováno v:
Breast, Vol 79, Iss , Pp 103829- (2025)
Background: Individual risk prediction of 5-year locoregional recurrence (LRR) and contralateral breast cancer (CBC) supports decisions regarding personalised surveillance. The previously developed INFLUENCE tool was rebuild, including a recent popul
Externí odkaz:
https://doaj.org/article/2694b70dbad34b3ab649535c9ac0aeed