对比经典模型与大模型在新冠死亡预测中的表现，发现微调后的大模型接近经典模型效果。
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
- 将表格数据转为文本，用大模型做零样本分类，部分模型经微调显著提升性能。
- 微调后的Mistral-7b召回率达79%，外部验证F1达0.74，接近XGBoost的0.87。
- 适合医疗预测研究者参考，尤其关注如何用大模型处理结构化医学数据。
本研究比较了经典特征驱动机器学习模型（CMLs）与大语言模型（LLMs）在使用来自四家医院共9,134名患者高维表格数据预测新冠死亡率上的表现。评估了七种CML模型（包括XGBoost和随机森林（RF））以及八种LLM（如GPT-4和Mistral-7b），后者对转换为文本的结构化数据进行零样本分类。此外，使用QLoRA方法微调了Mistral-7b。XGBoost和RF在内部与外部验证中分别取得0.87和0.83的F1分数，表现最优。GPT-4在LLM中领先，F1为0.43；而微调后的Mistral-7b召回率从1%提升至79%，外部验证时稳定达到0.74的F1分数。尽管大模型在零样本分类中表现一般，但微调显著提升了其效果，可能缩小与传统模型的差距。然而，在处理高维表格数据任务上，经典模型仍具优势。该研究突显了CMLs与微调后LLMs在医学预测建模中的潜力，同时强调当前经典模型在结构化数据分析中的领先地位。
原文摘要 · Abstract (English)
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral- 7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.
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