用患者历史报告提升胸部X光自动诊断准确率
HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
- 融合五年历史影像与报告，构建时序数据集
- 诊断准确率提升：AUROC+6.56%，AUPRC+9.51%
- 适合需长期病史分析的临床辅助诊断场景
胸部X光是检测胸腔异常的常用无创诊断工具。尽管已有多种AI模型辅助放射科医生解读图像，但多数模型忽略了患者的既往病史。为此，我们构建了Temporal MIMIC数据集，整合了来自MIMIC-CXR和MIMIC-IV的五年患者历史数据，包含12,221名患者和13种病理类型。在此基础上，提出HIST-AID框架，利用历史报告增强自动诊断精度。实验表明，相比仅依赖影像的模型，本方法在AUROC上提升6.56%，在AUPRC上提升9.51%，且在不同性别、年龄和种族群体中均表现稳定。研究发现，近期数据有助于性能提升，而过时数据可能因病情变化反而降低准确性。本工作展示了融合历史数据对实现更可靠自动诊断的潜力，为临床决策提供关键支持。
原文摘要 · Abstract (English)
Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool for detecting thoracic abnormalities. While numerous AI models assist radiologists in interpreting these images, most overlook patients' historical data. To bridge this gap, we introduce Temporal MIMIC dataset, which integrates five years of patient history, including radiographic scans and reports from MIMIC-CXR and MIMIC-IV, encompassing 12,221 patients and thirteen pathologies. Building on this, we present HIST-AID, a framework that enhances automatic diagnostic accuracy using historical reports. HIST-AID emulates the radiologist's comprehensive approach, leveraging historical data to improve diagnostic accuracy. Our experiments demonstrate significant improvements, with AUROC increasing by 6.56% and AUPRC by 9.51% compared to models that rely solely on radiographic scans. These gains were consistently observed across diverse demographic groups, including variations in gender, age, and racial categories. We show that while recent data boost performance, older data may reduce accuracy due to changes in patient conditions. Our work paves the potential of incorporating historical data for more reliable automatic diagnosis, providing critical support for clinical decision-making.
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