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Found 7 papers, 0 papers with code
Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting
We conduct comprehensive ablation studies to demonstrate the trade-offs between the number of tunable parameters and forecasting performance and assess the impact of varying LoRA matrix ranks on model performance.
Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps
This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical time steps in the forecasting process, thus improving model interpretability and supporting clinical decision-making.
SM70: A Large Language Model for Medical Devices
We are introducing SM70, a 70 billion-parameter Large Language Model that is specifically designed for SpassMed's medical devices under the brand name 'JEE1' (pronounced as G1 and means 'Life').
Vital Sign Forecasting for Sepsis Patients in ICUs
This paper uses state-of-the-art deep learning (DL) architectures to introduce a multi-step forecasting system to predict vital signs indicative of septic shock progression in Intensive Care Units (ICUs).
Extending Machine Learning-Based Early Sepsis Detection to Different Demographics
Sepsis requires urgent diagnosis, but research is predominantly focused on Western datasets.
Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients
We analyze the samples where the forecasted trend does not match the actual trend and study the impact of infused drugs on changing the actual vital signs compared to the forecasted trend.
Investigating Poor Performance Regions of Black Boxes: LIME-based Exploration in Sepsis Detection
Interpreting machine learning models remains a challenge, hindering their adoption in clinical settings.
