为电子病历设计可个性化、低成本的增量特征选择方法，提升诊疗决策效率。
P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Records
- 按患者个性化在线筛选特征，结合预算与成本约束
- 在预算内逐步获取最有信息量的特征，提升诊断信心
- 适合临床筛查场景，帮助医生高效利用医疗资源
电子健康记录（EHR）通过数字化患者数据革新了医疗体系，提升了数据可及性并优化了临床流程。然而，从复杂且多模态的EHR数据中提取有效洞察仍具挑战，传统特征选择方法常因数据稀疏性和异质性而表现不佳，尤其难以兼顾患者个体差异和临床特征获取成本。为此，本文提出一种面向EHR数据的个性化、在线、成本敏感的特征选择框架。该框架以个体患者为单位，采用在线方式逐次获取特征，同时考虑预算限制和特征变异成本。所提方法能有效处理稀疏与多模态数据，在多样医疗环境中实现稳健且可扩展的性能。主要应用场景为支持医生在患者筛查中的决策。通过引导医生在预算范围内逐步获取最具信息量的特征，本方法旨在提高诊断置信度并优化资源使用效率。
原文摘要 · Abstract (English)
Electronic Health Records (EHR) have revolutionized healthcare by digitizing patient data, improving accessibility, and streamlining clinical workflows. However, extracting meaningful insights from these complex and multimodal datasets remains a significant challenge for researchers. Traditional feature selection methods often struggle with the inherent sparsity and heterogeneity of EHR data, especially when accounting for patient-specific variations and feature costs in clinical applications. To address these challenges, we propose a novel personalized, online and cost-aware feature selection framework tailored specifically for EHR datasets. The features are aquired in an online fashion for individual patients, incorporating budgetary constraints and feature variability costs. The framework is designed to effectively manage sparse and multimodal data, ensuring robust and scalable performance in diverse healthcare contexts. A primary application of our proposed method is to support physicians' decision making in patient screening scenarios. By guiding physicians toward incremental acquisition of the most informative features within budget constraints, our approach aims to increase diagnostic confidence while optimizing resource utilization.
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。