用电子病历数据提前一年预警胰腺癌，提升早期发现能力。
Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Records
- 融合诊断码与化验数据，用神经微分方程建模时间序列。
- 在近4700名患者上实现AUC提升6.5%至15.5%。
- 识别出已知和新发现的胰腺癌风险指标，适合临床筛查应用。
胰腺导管腺癌（PDAC）是致死率极高的癌症，早期检测因缺乏特异性症状和可靠生物标志物而面临重大挑战。本文提出一种新型多模态方法，整合电子健康记录中的纵向诊断码历史与常规实验室检测数据，可将PDAC的检测时间提前至临床诊断前一年。该方法结合神经控制微分方程建模不规则的化验时间序列，利用预训练语言模型与循环网络学习诊断码轨迹表示，并通过交叉注意力机制捕捉两模态间的交互关系。我们在近4,700名患者的实证数据集上验证该方法，相较于现有最优模型，AUC提升达6.5%至15.5%。此外，模型识别出与高胰腺癌风险相关的诊断码及实验室检测组合，涵盖已知和新发现的生物标志物。代码已开源：https://github.com/MosbahAouad/EarlyPDAC-MML。
原文摘要 · Abstract (English)
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, and early detection remains a major clinical challenge due to the absence of specific symptoms and reliable biomarkers. In this work, we propose a new multimodal approach that integrates longitudinal diagnosis code histories and routinely collected laboratory measurements from electronic health records to detect PDAC up to one year prior to clinical diagnosis. Our method combines neural controlled differential equations to model irregular lab time series, pretrained language models and recurrent networks to learn diagnosis code trajectory representations, and cross-attention mechanisms to capture interactions between the two modalities. We develop and evaluate our approach on a real-world dataset of nearly 4,700 patients and achieve significant improvements in AUC ranging from 6.5% to 15.5% over state-of-the-art methods. Furthermore, our model identifies diagnosis codes and laboratory panels associated with elevated PDAC risk, including both established and new biomarkers. Our code is available at https://github.com/MosbahAouad/EarlyPDAC-MML.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。