结合服药情况监测阿尔茨海默患者金融风险，提升高危事件识别率。
Medication-Aware Financial Exploitation Detection for Alzheimer's Patients Using Edge-Aware Interaction Risk Modeling
- 将服药依从性与交易行为同步分析，构建风险感知模型。
- 在服药后认知脆弱期，召回率从0.7442提升至0.9070。
- 适合医疗金融风控、老年健康监护领域研究者参考。
阿尔茨海默病患者在认知能力下降期间面临日益严重的金融剥削风险。传统欺诈检测系统仅依赖财务行为，忽视了可能影响脆弱性的临床因素。本文提出一种药物感知框架，将服药依从性与交易级监控同步，以提升对认知高风险金融事件的检测能力。基于180名患者在45天内的数据，构建了混合仿真数据集，包含8,100条服药记录和30,855笔交易。通过金融独占、药物增强及交互感知三类逻辑回归模型评估金额异常、商户新颖性、交易频率、时间偏差及服药依从性。结果表明，金融独占基线全球F1分数为0.5000，而交互感知模型在药物诱导的脆弱窗口期内召回率从0.7442提升至0.9070，并在高风险案例排序中达到最高平均精度。研究提示，服药依从性更适合作为金融风险的情境调节因子，而非独立预测指标。
原文摘要 · Abstract (English)
Financial exploitation is a growing concern for people with Alzheimer's disease, especially during periods of reduced cognitive stability. Conventional fraud detection systems usually rely on financial behavior alone and ignore clinically relevant factors that may alter vulnerability. This paper proposes a medication-aware framework that synchronizes medication adherence with transaction-level monitoring to improve detection of cognitively risky financial events. A hybrid simulation dataset was constructed for 180 patients across 45 days, producing 8,100 medication records and 30,855 transactions. The framework evaluates amount anomaly, vendor novelty, transaction frequency, time deviation, and medication adherence through financial-only, additive medication-aware, and interaction-aware logistic models. Results show that the financial-only baseline obtained the highest global F1-score of 0.5000, but the interaction-aware model improved recall during medication-induced vulnerability windows from 0.7442 to 0.9070 and achieved the highest average precision for ranked high-risk cases. The findings suggest that medication adherence is most useful as a contextual modifier of financial risk rather than as an isolated predictor.
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