用电子病历预测化验结果，支持连续数值输出。
LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records
- 基于语言建模统一预测多种化验指标
- 在3个数据集上优于传统模型与大模型
- 适合临床决策支持与早期预警场景
化验检查是疾病诊断和患者状态监测的基础，但频繁检测对患者负担重，且结果常延迟。为此，我们提出LabTOP，一种基于语言建模的统一模型，利用电子健康记录（EHR）预测化验结果。与仅估算部分化验或分类离散值范围的传统方法不同，LabTOP可对多种化验项目进行连续数值预测。我们在三个公开的EHR数据集上评估该模型，结果表明其性能超越传统机器学习模型及前沿的大语言模型。通过大量消融实验验证了设计选择的有效性。我们认为LabTOP可作为准确、通用的化验结果预测框架，适用于临床决策支持与危重症早期发现。
原文摘要 · Abstract (English)
Lab tests are fundamental for diagnosing diseases and monitoring patient conditions. However, frequent testing can be burdensome for patients, and test results may not always be immediately available. To address these challenges, we propose LabTOP, a unified model that predicts lab test outcomes by leveraging a language modeling approach on EHR data. Unlike conventional methods that estimate only a subset of lab tests or classify discrete value ranges, LabTOP performs continuous numerical predictions for a diverse range of lab items. We evaluate LabTOP on three publicly available EHR datasets and demonstrate that it outperforms existing methods, including traditional machine learning models and state-of-the-art large language models. We also conduct extensive ablation studies to confirm the effectiveness of our design choices. We believe that LabTOP will serve as an accurate and generalizable framework for lab test outcome prediction, with potential applications in clinical decision support and early detection of critical conditions.
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