通过诊断信号质量评估，提升痴呆预测模型在不同医院数据中的泛化能力。
Signal Fidelity Index-Aware Calibration for Dementia Predictions Across Heterogeneous Real-World Data
- 构建患者级诊断信号质量指数（SFI），融合六项可解释指标。
- 无标签情况下校准后召回率提升32.5%，F1分数提高26.1%。
- 适合缺乏结局标签的大规模医疗数据预测场景。
机器学习模型在电子健康记录（EHR）上训练后，常因分布偏移导致跨医疗机构性能下降。一个基本但未被充分研究的因素是诊断信号衰减：不同机构间诊断质量和一致性差异，影响编码用于训练与预测的可靠性。本文提出一种患者级痴呆诊断信号质量指数（SFI），并测试其在无结果标签情况下的校准效果。我们构建了2,500个合成数据集，每组含1,000名患者，模拟真实人口统计、就诊和编码模式。SFI由六个可解释成分构成：诊断特异性、时间一致性、熵、上下文一致性、药物匹配度与轨迹稳定性。采用乘法调整进行SFI-aware校准，在50个仿真批次中优化参数。在最优参数α=2.0时，所有指标显著提升（p<0.001）：平衡准确率提升10.3%，召回率提升32.5%，精确率提升31.9%，F1分数提升26.1%。性能接近参考标准，F1与召回率误差小于1%，平衡准确率和检出率分别提升52.3%和41.1%。结论表明，诊断信号衰减是可处理的泛化障碍，SFI-aware校准提供了一种实用且无需标签的策略，尤其适用于大规模行政数据中缺乏结局标签的场景。
原文摘要 · Abstract (English)
\textbf{Background:} Machine learning models trained on electronic health records (EHRs) often degrade across healthcare systems due to distributional shift. A fundamental but underexplored factor is diagnostic signal decay: variability in diagnostic quality and consistency across institutions, which affects the reliability of codes used for training and prediction. \textbf{Objective:} To develop a Signal Fidelity Index (SFI) quantifying diagnostic data quality at the patient level in dementia, and to test SFI-aware calibration for improving model performance across heterogeneous datasets without outcome labels. \textbf{Methods:} We built a simulation framework generating 2,500 synthetic datasets, each with 1,000 patients and realistic demographics, encounters, and coding patterns based on dementia risk factors. The SFI was derived from six interpretable components: diagnostic specificity, temporal consistency, entropy, contextual concordance, medication alignment, and trajectory stability. SFI-aware calibration applied a multiplicative adjustment, optimized across 50 simulation batches. \textbf{Results:} At the optimal parameter ($α$ = 2.0), SFI-aware calibration significantly improved all metrics (p $<$ 0.001). Gains ranged from 10.3\% for Balanced Accuracy to 32.5\% for Recall, with notable increases in Precision (31.9\%) and F1-score (26.1\%). Performance approached reference standards, with F1-score and Recall within 1\% and Balanced Accuracy and Detection Rate improved by 52.3\% and 41.1\%, respectively. \textbf{Conclusions:} Diagnostic signal decay is a tractable barrier to model generalization. SFI-aware calibration provides a practical, label-free strategy to enhance prediction across healthcare contexts, particularly for large-scale administrative datasets lacking outcome labels.
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