不微调患者数据也能生成高效医疗预测模型，保护隐私。
PatientDx: Merging Large Language Models for Protecting Data-Privacy in Healthcare
- 通过模型合并技术构建医疗预测模型，无需在患者数据上训练。
- 在MIMIC-IV数据集上死亡预测任务的AUROC提升最高达7%。
- 避免数据泄露风险，适合对隐私要求高的医疗AI应用。
大语言模型（LLM）的微调虽能提升任务性能，但需大量标注数据，尤其在医疗领域易引发隐私问题。本文提出PatientDx框架，通过模型合并技术，在不微调或直接接触患者数据的前提下，构建高效的医疗预测模型。该方法基于最新提出的模型合并策略，利用一个适配数值推理的主模型，并在示例基础上优化超参数，不进行模型训练。实验表明，在MIMIC-IV数据集的死亡预测任务中，相比初始模型，性能提升最高达7%的AUROC。此外，与微调模型相比，本方法更不易发生数据泄露，且性能相当。案例研究进一步验证了其有效性。最佳模型已公开发布于Hugging Face：https://huggingface.co/Jgmorenof/mistral_merged_0_4。
原文摘要 · Abstract (English)
Fine-tuning of Large Language Models (LLMs) has become the default practice for improving model performance on a given task. However, performance improvement comes at the cost of training on vast amounts of annotated data which could be sensitive leading to significant data privacy concerns. In particular, the healthcare domain is one of the most sensitive domains exposed to data privacy issues. In this paper, we present PatientDx, a framework of model merging that allows the design of effective LLMs for health-predictive tasks without requiring fine-tuning nor adaptation on patient data. Our proposal is based on recently proposed techniques known as merging of LLMs and aims to optimize a building block merging strategy. PatientDx uses a pivotal model adapted to numerical reasoning and tunes hyperparameters on examples based on a performance metric but without training of the LLM on these data. Experiments using the mortality tasks of the MIMIC-IV dataset show improvements up to 7% in terms of AUROC when compared to initial models. Additionally, we confirm that when compared to fine-tuned models, our proposal is less prone to data leak problems without hurting performance. Finally, we qualitatively show the capabilities of our proposal through a case study. Our best model is publicly available at https://huggingface.co/ Jgmorenof/mistral\_merged\_0\_4.
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