传统计数模型在电子病历预测中仍表现强劲，不输最新大模型流水线。
Count-Based Approaches Remain Strong: A Benchmark Against Transformer and LLM Pipelines on Structured EHR
- 用本体汇总+时间分段构建计数模型，对比了轻量级和基础表格模型
- 8个临床预测任务中，计数模型与大模型流水线胜负各半
- 适合看重可解释性与简单高效的医疗数据应用
结构化电子健康记录（EHR）对临床预测至关重要。尽管基于计数的模型在该数据上持续表现优异，但尚未有研究直接将其与近年报道在多种NLP任务中优于单个大模型的混合代理大模型流水线进行比较。本研究使用EHRSHOT数据集评估了三类方法：基于本体汇总并分两个时间窗口的计数模型（采用LightGBM和表格基础模型TabPFN）、预训练序列变换器CLMBR，以及将表格历史转换为自然语言摘要后由文本分类器处理的混合代理流水线。在八个临床预测任务上进行评估。结果显示，在八项任务中，计数模型与混合代理方法胜负相当。鉴于其简洁性和可解释性，计数模型仍是结构化EHR基准中的有力候选方案。源代码已公开于：https://github.com/cristea-lab/Structured_EHR_Benchmark。
原文摘要 · Abstract (English)
Structured electronic health records (EHR) are essential for clinical prediction. While count-based learners continue to perform strongly on such data, no benchmarking has directly compared them against more recent mixture-of-agents LLM pipelines, which have been reported to outperform single LLMs in various NLP tasks. In this study, we evaluated three categories of methodologies for EHR prediction using the EHRSHOT dataset: count-based models built from ontology roll-ups with two time bins, based on LightGBM and the tabular foundation model TabPFN; a pretrained sequential transformer (CLMBR); and a mixture-of-agents pipeline that converts tabular histories to natural-language summaries followed by a text classifier. We assessed eight outcomes using the EHRSHOT dataset. Across the eight evaluation tasks, head-to-head wins were largely split between the count-based and the mixture-of-agents methods. Given their simplicity and interpretability, count-based models remain a strong candidate for structured EHR benchmarking. The source code is available at: https://github.com/cristea-lab/Structured_EHR_Benchmark.
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