用排序对比损失构建语音健康监测的病情严重度评分
Comparator Loss: An Ordinal Contrastive Loss to Derive a Severity Score for Speech-based Health Monitoring
- 设计新型排序对比损失，确保评分符合诊断或时间顺序
- 轻标注数据训练模型即可区分神经退行性疾病患者与健康人
- 评分能预测未参与训练的临床指标，适合小样本医疗研究
神经退行性疾病（NDD）进展监测对治疗规划和新药评估具有重要意义。现有研究多聚焦于区分患者与健康对照，或预测真实世界健康指标，本文提出一种新方法：基于比较器损失（comparator loss）训练模型，生成可反映疾病进展的严重度评分。该损失强制评分满足诊断、临床评分或录音时间的有序关系。所提方法能融合不同来源的健康指标，有助于充分利用小规模健康数据集。实验表明，仅需轻度标注数据训练的模型即可有效区分NDD患者与健康个体，且其生成的评分与训练中未使用的标注（如ALSFRS-R评分及言语治疗师评估）具有显著相关性。
原文摘要 · Abstract (English)
Monitoring the progression of neurodegenerative disease (NDD) has important applications in planning treatment and evaluating new medications. Whereas much work has focused on discriminating patients from healthy controls, or predicting real-world health metrics, we propose a novel measure of disease progression: the severity score, derived from a model trained to minimize what we call the comparator loss. This loss ensures scores obey an ordering relation, based on diagnosis, clinical scores, or simply chronological order of recordings. The proposed comparator loss-based system has the potential to incorporate information from disparate health metrics, critical for making full use of small health-related datasets. We show that a model trained on lightly annotated data is capable of distinguishing between subjects with NDDs and healthy controls. Our score also correlates with annotations not observed in training, such as ALSFRS-R and those of speech and language therapists.
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