Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Schürch, Manuel"'
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:
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:
Zheng, Xiaochen, Chen, Xingyu, Schürch, Manuel, Mollaysa, Amina, Allam, Ahmed, Krauthammer, Michael
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discriminati
Externí odkaz:
http://arxiv.org/abs/2303.18205
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering. They constitute a powerful kernelized non-parametric method with well-calibrated uncertai
Externí odkaz:
http://arxiv.org/abs/2112.09519
Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit approximations of the covariance kernel have shown to be competitive. In this work we exploit a recent
Externí odkaz:
http://arxiv.org/abs/2007.06363
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger
Externí odkaz:
http://arxiv.org/abs/1905.11711