Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models
This work addresses the problem of forecasting kidney function decline for nephrologists and ML researchers, but it is incremental as it applies existing foundation models to a medical task with limited data.
The study tackled the challenge of predicting future estimated Glomerular Filtration Rate (eGFR) levels for kidney function monitoring by using Large Multimodal Models (LMMs) with prompting techniques and ensembles on a dataset of 50 patients, achieving predictive performance comparable to existing machine learning models.
The estimated Glomerular Filtration Rate (eGFR) is an essential indicator of kidney function in clinical practice. Although traditional equations and Machine Learning (ML) models using clinical and laboratory data can estimate eGFR, accurately predicting future eGFR levels remains a significant challenge for nephrologists and ML researchers. Recent advances demonstrate that Large Language Models (LLMs) and Large Multimodal Models (LMMs) can serve as robust foundation models for diverse applications. This study investigates the potential of LMMs to predict future eGFR levels with a dataset consisting of laboratory and clinical values from 50 patients. By integrating various prompting techniques and ensembles of LMMs, our findings suggest that these models, when combined with precise prompts and visual representations of eGFR trajectories, offer predictive performance comparable to existing ML models. This research extends the application of foundation models and suggests avenues for future studies to harness these models in addressing complex medical forecasting challenges.