Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Krones, Felix"'
This work presents our team's (SignalSavants) winning contribution to the 2024 George B. Moody PhysioNet Challenge. The Challenge had two goals: reconstruct ECG signals from printouts and classify them for cardiac diseases. Our focus was the first ta
Externí odkaz:
http://arxiv.org/abs/2410.14185
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest
This work showcases our team's (The BEEGees) contributions to the 2023 George B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from coma following cardiac arrest using clinical data and time-series such as multi-channel EEG
Externí odkaz:
http://arxiv.org/abs/2403.06027
Machine learning methods in healthcare have traditionally focused on using data from a single modality, limiting their ability to effectively replicate the clinical practice of integrating multiple sources of information for improved decision making.
Externí odkaz:
http://arxiv.org/abs/2402.02460
Large language models (LLMs) have made rapid improvement on medical benchmarks, but their unreliability remains a persistent challenge for safe real-world uses. To design for the use LLMs as a category, rather than for specific models, requires devel
Externí odkaz:
http://arxiv.org/abs/2310.07225
Publikováno v:
Computing in Cardiology, vol. 49, 2022
This study presents our team PathToMyHeart's contribution to the George B. Moody PhysioNet Challenge 2022. Two models are implemented. The first model is a Dual Bayesian ResNet (DBRes), where each patient's recording is segmented into overlapping log
Externí odkaz:
http://arxiv.org/abs/2305.16691
Publikováno v:
In Information Fusion February 2025 114