5 papers
Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps
Yuwei Liu, Chen Dan, Anubhav Bhatti +4
Sepsis is a leading cause of mortality in intensive care units (ICUs), representing a substantial medical challenge. The complexity of analyzing diverse vital signs to predict seps…
Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting
Divij Gupta, Anubhav Bhatti, Suraj Parmar +4
Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. However, its application to ti…
SM70: A Large Language Model for Medical Devices
Anubhav Bhatti, Surajsinh Parmar, San Lee
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…
Vital Sign Forecasting for Sepsis Patients in ICUs
Anubhav Bhatti, Yuwei Liu, Chen Dan +4
Sepsis and septic shock are a critical medical condition affecting millions globally, with a substantial mortality rate. This paper uses state-of-the-art deep learning (DL) archite…
Extending Machine Learning-Based Early Sepsis Detection to Different Demographics
Surajsinh Parmar, Tao Shan, San Lee +2
Sepsis requires urgent diagnosis, but research is predominantly focused on Western datasets. In this study, we perform a comparative analysis of two ensemble learning methods, Ligh…