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pro vyhledávání: '"A Udén"'
This research focuses on evaluating the non-commercial open-source large language models (LLMs) Meditron, MedAlpaca, Mistral, and Llama-2 for their efficacy in interpreting medical guidelines saved in PDF format. As a specific test scenario, we appli
Externí odkaz:
http://arxiv.org/abs/2405.03359
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
Autor:
Luo, Jerry, Paduraru, Cosmin, Voicu, Octavian, Chervonyi, Yuri, Munns, Scott, Li, Jerry, Qian, Crystal, Dutta, Praneet, Davis, Jared Quincy, Wu, Ningjia, Yang, Xingwei, Chang, Chu-Ming, Li, Ted, Rose, Rob, Fan, Mingyan, Nakhost, Hootan, Liu, Tinglin, Kirkman, Brian, Altamura, Frank, Cline, Lee, Tonker, Patrick, Gouker, Joel, Uden, Dave, Bryan, Warren Buddy, Law, Jason, Fatiha, Deeni, Satra, Neil, Rothenberg, Juliet, Waraich, Mandeep, Carlin, Molly, Tallapaka, Satish, Witherspoon, Sims, Parish, David, Dolan, Peter, Zhao, Chenyu, Mankowitz, Daniel J.
This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted
Externí odkaz:
http://arxiv.org/abs/2211.07357
Clinical word embeddings are extensively used in various Bio-NLP problems as a state-of-the-art feature vector representation. Although they are quite successful at the semantic representation of words, due to the dataset - which potentially carries
Externí odkaz:
http://arxiv.org/abs/2208.01341
Cognitive impairment in young adults following cerebellar stroke: Prevalence and longitudinal course
Autor:
van Alebeek, Mayte E., Norden, Anouk van, Brouwers, Paul J.A.M., Arntz, Renate M., van Dijk, Gert W., Gons, Rob A.R., van Uden, Inge W.M., Heijer, Tom den, de Kort, Paul L.M., de Laat, Karlijn F., Vermeer, Sarah E., van Zagten, Marian S.G., Wermer, Marieke J.H., Nederkoorn, Paul J., van Rooij, Frank G., van den Wijngaard, Ido R., Reumers, Stacha F.I., Schellekens, Mijntje M.I., Lugtmeijer, Selma, Maas, Roderick P.P.W.M., Verhoeven, Jamie I., Boot, Esther M., Ekker, Merel S., Tuladhar, Anil M., van de Warrenburg, Bart P.C., Schutter, Dennis J.L.G., Kessels, Roy P.C., de Leeuw, Frank-Erik
Publikováno v:
In Cortex September 2024 178:104-115
Autor:
Spronk, I., van Uden, D., Lansdorp, C.A., van Dammen, L., van Gemert, R., Visser, I., Versluis, G., Wanders, H., Geelen, S.J.G., Verwilligen, R.A.F., van der Vlegel, M., Bijker, G.C., Heijblom, M.C., Fokke-Akkerman, M., Stoop, M., van Baar, M.E., Nieuwenhuis, M.K., Pijpe, A., van Schie, C.M.H., Gardien, K.L.M., Lucas, Y., Snoeks, A., Scholten-Jaegers, S.M.H.J., Meij-de Vries, A., Haanstra, T.M., Weel-Koenders, A.E.A.M., Wood, F.M., Edgar, D.W., Bosma, E., Middelkoop, E., van der Vlies, C.H., van Zuijlen, P.P.M.
Publikováno v:
In Burns September 2024 50(7):1925-1934
Autor:
Ankersmid, J.W., Drossaert, C.H.C., Strobbe, L.J.A., Hackert, M.Q.N., Engels, N., Prick, J.C.M., Teerenstra, S., van Riet, Y.E.A., The, R., van Uden-Kraan, C.F., Siesling, S.
Publikováno v:
In European Journal of Cancer December 2024 213