19 citations · 28 across the 10 of their papers we have counts for
6 papers · 1 filter
Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts
Felix Krones, Ben Walker, Terry Lyons +1
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 printo…
Improving In-Context Learning with Small Language Model Ensembles
M. Mehdi Mojarradi, Lingyi Yang, Robert McCraith +1
Large language models (LLMs) have shown impressive capabilities across various tasks, but their performance on domain-specific tasks remains limited. While methods like retrieval a…
Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering
Yushi Yang, Andrew M. Bean, Robert McCraith +1
Fine-tuning Large Language Models (LLMs) incurs considerable training costs, driving the need for data-efficient training with optimised data ordering. Human-inspired strategies of…
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest
Felix H. Krones, Ben Walker, Guy Parsons +2
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…
Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?
Harry Mayne, Guy Parsons, Adam Mahdi
The use of unsupervised learning to identify patient subgroups has emerged as a potentially promising direction to improve the efficiency of Intensive Care Units (ICUs). By identif…
Review of multimodal machine learning approaches in healthcare
Felix Krones, Umar Marikkar, Guy Parsons +2
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 int…