用大模型预测肾功能变化，效果媲美传统机器学习。
Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models
- 结合精准提示和轨迹图，用大多模态模型预测未来肾功能。
- 在50名患者数据上，预测性能达到现有机器学习水平。
- 适合关注医疗预测与大模型应用的临床与算法研究者。
估算肾小球滤过率（eGFR）是临床评估肾功能的关键指标。尽管传统公式和基于临床及检验数据的机器学习模型可估算eGFR，但准确预测未来eGFR水平仍是肾病科医生和机器学习研究者的重大挑战。近期研究表明，大型语言模型（LLMs）和大型多模态模型（LMMs）可作为多样化应用的稳健基础模型。本研究利用包含50名患者临床与检验数据的多模态数据集，探索了LMMs在预测未来eGFR水平方面的潜力。通过整合多种提示技术与LMM集成策略，结果表明，当结合精确提示和eGFR轨迹可视化时，这些模型的预测性能可媲美现有机器学习模型。该研究拓展了基础模型在复杂医疗预测中的应用，为未来研究提供了新方向。
原文摘要 · Abstract (English)
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.
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