提出可解释的帕金森病多模态评估框架，自动判断结果可信度并建议重测。
SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS
- 构建8维症状空间，融合语音、步态等多源数据统一表征
- 在5个真实数据集上实现最高AUC 0.953，MAE低至4.579
- 支持可靠性判断与症状溯源，适合临床长期随访应用
真实世界帕金森病评估面临模态异质性、跨设备偏差和标签不完整等挑战。现有方法多关注平均预测性能，缺乏回溯性可靠性评估机制——即判断模型何时可靠、何时应拒绝评估、何时需重测，以及预测依据来自哪些症状维度。本文提出SGC-RML，将语音、步态、可穿戴运动、移动任务和临床变量映射到共享的8维症状节点空间（7个临床症状节点和1个可靠性状态辅助节点），通过症状图谱统一运动与非运动表征。结合不确定性估计、共形校准与选择性决策路由，模型不仅能预测症状及严重程度，还能在证据不足时拒绝评估或建议重测。我们在五个真实世界帕金森病数据集上验证该框架，涵盖分类、回归、事件检测和纵向严重度预测。实验显示，SGC-RML在PPMI上取得MAE 4.579 / R² 0.772，mPower上AUC达0.953，PADS上AUC为0.825。在无泄漏时间锚定下，仅需5个个体特异性锚点，即可将UCI从几乎不可预测的非个体化设置（运动MAE 8.38，CCC 0.02）转化为校准后的纵向评估（运动MAE 3.24，CCC 0.756），且分段共形覆盖保持在0.80目标值。在Daphnet LOSO协议下，达到F1 0.803 / AUC 0.872。结果表明，SGC-RML提供了一种在多模态不完整条件下，兼具准确性、校准性、可审计性和症状可解释性的统一帕金森病回溯性纵向评估范式。
原文摘要 · Abstract (English)
Real-world digital Parkinson's disease assessment faces challenges such as heterogeneous modalities, cross-device bias, and incomplete labeling. Existing methods often focus on average predictive performance, lacking the reliability mechanisms needed for retrospective reliability-aware assessment - namely, determining when the model is reliable, when to reject an assessment, when to retest, and from which symptom dimensions the predictions are based. This paper proposes SGC-RML, which maps speech, gait, wearable motion, mobility tasks, and clinical variables to a shared 8-dimensional symptom node space (7 clinical symptom nodes and 1 reliability_state auxiliary node), unifying motor and non-motor representations through a symptom atlas. By jointly introducing uncertainty estimation, conformal calibration, and selective decision routing, the model can not only predict symptoms and severity but also reject assessments or suggest retests when evidence is insufficient. We validate this framework on five real-world PD datasets, covering classification, regression, event detection, and longitudinal severity prediction. Experiments show that SGC-RML achieves an MAE of 4.579 / R^2 of 0.772 on PPMI, an AUC of 0.953 on mPower, and an AUC of 0.825 on PADS. Under leak-free temporal anchoring, as few as 5 subject-specific anchors transform UCI from an essentially non-predictive subject-independent setting (motor MAE 8.38, CCC 0.02) into a calibrated longitudinal assessment (motor MAE 3.24, CCC 0.756) with split-conformal coverage held at the 0.80 target. Under the Daphnet LOSO protocol, it achieves an F1 of 0.803 / AUC of 0.872. These results demonstrate that SGC-RML provides a unified paradigm for accurate, calibrated, auditable, and symptom-interpretable retrospective longitudinal assessment of PD under incomplete multimodal conditions.
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