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Found 5 papers, 3 papers with code
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.
Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?
However, it is unclear whether common patient subgroups exist across different ICUs, which would determine whether ICU restructuring could be operationalised in a standardised manner.
Review of multimodal machine learning approaches in healthcare
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.
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
The second model is the output of DBRes integrated with demographic data and signal features using XGBoost. DBRes achieved our best weighted accuracy of $0. 771$ on the hidden test set for murmur classification, which placed us fourth for the murmur task.
Ranked #1 on
Classify murmurs
on CirCor DigiScope
Explaining Chest X-ray Pathologies in Natural Language
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice.
