Zobrazeno 1 - 10
of 2 845
pro vyhledávání: '"Schürch, P."'
Autor:
Mittmann, Gesa, Laiouar-Pedari, Sara, Mehrtens, Hendrik A., Haggenmüller, Sarah, Bucher, Tabea-Clara, Chanda, Tirtha, Gaisa, Nadine T., Wagner, Mathias, Klamminger, Gilbert Georg, Rau, Tilman T., Neppl, Christina, Compérat, Eva Maria, Gocht, Andreas, Hämmerle, Monika, Rupp, Niels J., Westhoff, Jula, Krücken, Irene, Seidl, Maximillian, Schürch, Christian M., Bauer, Marcus, Solass, Wiebke, Tam, Yu Chun, Weber, Florian, Grobholz, Rainer, Augustyniak, Jaroslaw, Kalinski, Thomas, Hörner, Christian, Mertz, Kirsten D., Döring, Constanze, Erbersdobler, Andreas, Deubler, Gabriele, Bremmer, Felix, Sommer, Ulrich, Brodhun, Michael, Griffin, Jon, Lenon, Maria Sarah L., Trpkov, Kiril, Cheng, Liang, Chen, Fei, Levi, Angelique, Cai, Guoping, Nguyen, Tri Q., Amin, Ali, Cimadamore, Alessia, Shabaik, Ahmed, Manucha, Varsha, Ahmad, Nazeel, Messias, Nidia, Sanguedolce, Francesca, Taheri, Diana, Baraban, Ezra, Jia, Liwei, Shah, Rajal B., Siadat, Farshid, Swarbrick, Nicole, Park, Kyung, Hassan, Oudai, Sakhaie, Siamak, Downes, Michelle R., Miyamoto, Hiroshi, Williamson, Sean R., Holland-Letz, Tim, Schneider, Carolin V., Kather, Jakob Nikolas, Tolkach, Yuri, Brinker, Titus J.
The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Glea
Externí odkaz:
http://arxiv.org/abs/2410.15012
Autor:
Vollenweider, Michael, Schürch, Manuel, Rohrer, Chiara, Gut, Gabriele, Krauthammer, Michael, Wicki, Andreas
Precision medicine has the potential to tailor treatment decisions to individual patients using machine learning (ML) and artificial intelligence (AI), but it faces significant challenges due to complex biases in clinical observational data and the h
Externí odkaz:
http://arxiv.org/abs/2410.00509
Autor:
Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Launay, David, Airò, Paolo, Bečvář, Radim, Denton, Christopher, Radic, Mislav, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR
We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the un
Externí odkaz:
http://arxiv.org/abs/2407.11427
Autor:
Zheng, Xiaochen, Schürch, Manuel, Chen, Xingyu, Komninou, Maria Angeliki, Schüpbach, Reto, Allam, Ahmed, Bartussek, Jan, Krauthammer, Michael
The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient characteristics. Postoperative delirium (POD) is a complex neuropsychiatric c
Externí odkaz:
http://arxiv.org/abs/2405.03327
Autor:
Schürch, Manuel, Boos, Laura, Heinzelmann-Schwarz, Viola, Gut, Gabriele, Krauthammer, Michael, Wicki, Andreas, Consortium, Tumor Profiler
AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction between complex patient characteristics and their corresponding treatment outcomes. New technolo
Externí odkaz:
http://arxiv.org/abs/2402.12190
Autor:
Feller, C., Pommerol, A., Lethuillier, A., Hänni, N., Schürch, S., Bühr, C., Gundlach, B., Haenni, B., Jäggi, N., Kaminek, M., Team, the CoPhyLab
Objective: In the framework of the Cometary Physics Laboratory (CoPhyLab) and its sublimation experiments of cometary surface analogues under simulated space conditions, we characterize the properties of intimate mixtures of juniper charcoal and SiO$
Externí odkaz:
http://arxiv.org/abs/2312.08311
Autor:
Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representations of an underlying generati
Externí odkaz:
http://arxiv.org/abs/2311.08149
Autor:
Chen, Xingyu, Zheng, Xiaochen, Mollaysa, Amina, Schürch, Manuel, Allam, Ahmed, Krauthammer, Michael
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampling rates (i.e., recordings/measurement) across these features. Modeli
Externí odkaz:
http://arxiv.org/abs/2311.07744
Autor:
Schürch, Manuel, Li, Xiang, Allam, Ahmed, Rathmes, Giulia, Mollaysa, Amina, Cavelti-Weder, Claudia, Krauthammer, Michael
Publikováno v:
Machine Learning for Health (ML4H) 2023
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personal
Externí odkaz:
http://arxiv.org/abs/2309.16521
Autor:
Ilona Hagelstein, Laura Wessling, Alexander Rochwarger, Latifa Zekri, Boris Klimovich, Christian M. Tegeler, Gundram Jung, Christian M. Schürch, Helmut R. Salih, Martina S. Lutz
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-15 (2024)
Abstract Background Breast cancer (BC) is the most common malignancy in women. Immunotherapy has revolutionized treatment options in many malignancies, and the introduction of immune checkpoint inhibition yielded beneficial results also in BC. Howeve
Externí odkaz:
https://doaj.org/article/4fe2b27d65564a37932f57ac8d6d6e06
