Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models

TL;DR

This paper explores the use of large multimodal models to predict future kidney function decline by integrating clinical data and visual eGFR trajectories, showing performance comparable to traditional ML methods.

Contribution

It demonstrates the potential of large multimodal models with prompting and ensemble techniques for accurate eGFR prediction, extending foundation model applications in nephrology.

Findings

LMMs achieve comparable accuracy to existing ML models in eGFR prediction.

Prompting and visual trajectory representations enhance model performance.

The approach opens new avenues for medical forecasting using foundation models.

Abstract

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.