1 code implementation • 31 Mar 2023 • Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, Michael Krauthammer
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification.
1 code implementation • Findings (EMNLP) 2021 • Farhad Nooralahzadeh, Nicolas Perez Gonzalez, Thomas Frauenfelder, Koji Fujimoto, Michael Krauthammer
Inspired by Curriculum Learning, we propose a consecutive (i. e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps.
1 code implementation • 11 Nov 2014 • Tobias Kuhn, Christine Chichester, Michael Krauthammer, Michel Dumontier
Making available and archiving scientific results is for the most part still considered the task of classical publishing companies, despite the fact that classical forms of publishing centered around printed narrative articles no longer seem well-suited in the digital age.
Digital Libraries
1 code implementation • 24 Dec 2020 • Kyriakos Schwarz, Ahmed Allam, Nicolas Andres Perez Gonzalez, Michael Krauthammer
Background: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves.
1 code implementation • 30 Dec 2019 • Laura Kinkead, Ahmed Allam, Michael Krauthammer
Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional.
1 code implementation • 14 Nov 2023 • Cécile Trottet, Manuel Schürch, Ahmed Allam, Imon Barua, Liubov Petelytska, Oliver Distler, Anna-Maria Hoffmann-Vold, Michael Krauthammer, the EUSTAR collaborators
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories.
1 code implementation • 3 Oct 2022 • Kyriakos Schwarz, Alicia Pliego-Mendieta, Lara Planas-Paz, Chantal Pauli, Ahmed Allam, Michael Krauthammer
We use information from the largest drug combination database available (DrugComb), combining drug synergy scores in order to construct high confidence benchmark datasets.
1 code implementation • 22 Dec 2018 • Ahmed Allam, Mate Nagy, George Thoma, Michael Krauthammer
Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0. 642 AUC (95% CI, 0. 640-0. 645).
no code implementations • 14 May 2020 • Ahmed Allam, Matthias Dittberner, Anna Sintsova, Dominique Brodbeck, Michael Krauthammer
Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records.
no code implementations • 18 Jul 2022 • Kyriakos Schwarz, Daniel Trejo Banos, Giulia Rathmes, Michael Krauthammer
In recent decades, there has been an increase in polypharmacy, the concurrent administration of multiple drugs per patient.
no code implementations • 8 Feb 2023 • Aron N. Horvath, Matteo Berchier, Farhad Nooralahzadeh, Ahmed Allam, Michael Krauthammer
Methods: We present an extensive evaluation of the impact of different federation and differential privacy techniques when training models on the open-source MIMIC-III dataset.
no code implementations • 8 May 2023 • Sanghwan Kim, Farhad Nooralahzadeh, Morteza Rohanian, Koji Fujimoto, Mizuho Nishio, Ryo Sakamoto, Fabio Rinaldi, Michael Krauthammer
To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports.
no code implementations • 28 Sep 2023 • Manuel Schürch, Xiang Li, Ahmed Allam, Giulia Rathmes, Amina Mollaysa, Claudia Cavelti-Weder, Michael Krauthammer
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.
no code implementations • 4 Oct 2023 • Amina Mollaysa, Ahmed Allam, Michael Krauthammer
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
no code implementations • 13 Nov 2023 • Xingyu Chen, Xiaochen Zheng, Amina Mollaysa, Manuel Schürch, Ahmed Allam, Michael Krauthammer
Here, we introduce TADA, a Two-stageAggregation process with Dynamic local Attention to harmonize time-wise and feature-wise irregularities in multivariate time series.
no code implementations • 13 Nov 2023 • Amina Mollaysa, Ahmed Allam, Michael Krauthammer
To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence.
no code implementations • 28 Nov 2023 • Amos Calamida, Farhad Nooralahzadeh, Morteza Rohanian, Koji Fujimoto, Mizuho Nishio, Michael Krauthammer
Furthermore, we demonstrate that one of our checkpoints exhibits a high correlation with human judgment, as assessed using the publicly available annotations of six board-certified radiologists, using a set of 200 reports.
no code implementations • 19 Feb 2024 • Manuel Schürch, Laura Boos, Viola Heinzelmann-Schwarz, Gabriele Gut, Michael Krauthammer, Andreas Wicki, Tumor Profiler Consortium
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.