防泄露基准与保形选择性分诊用于基于子宫电图的早产预测
A Leakage-Proof Benchmark and Conformal Selective Triage for Electrohysterogram-Based Preterm Birth Prediction
AI总结：
该研究针对基于子宫电图的早产预测，建立了患者独立的防泄露基准，采用92特征弹性网络逻辑模型结合类条件保形选择性预测，在300条记录（含38例早产）上验证，为分段生理数据确立了记录分离的验证原则，明确了保形预测的不确定性决策机制。
AI中文摘要：
早产仍是全球新生儿发病和死亡的主要原因。子宫电图（EHG）是一种无创性子宫肌电活动测量方法，已被用于早产预测，但当同一产妇记录的分段被拆分到训练和验证数据中时，性能估计可能存在偏差。我们明确了分段级与患者独立的记录分组验证的区别，在Term-Preterm Electrohysterogram Database（早产生离子宫电图数据库）上建立了患者独立基准，并评估了类条件保形选择性预测。我们在三个预设范围内分析了全部300条记录（其中38例为早产），采用记录分组嵌套交叉验证评估了92特征的弹性网络逻辑模型；预处理、模型拟合、Platt校准和保形估计均限定在训练数据中，性能通过严格的折外记录级预测计算，采用1000次自助重抽样。在患者独立评估下，AUROC为0.493（95%置信区间0.467-0.520），AUPRC为0.122（0.095-0.152），Brier评分为0.115（0.094-0.137）；在妊娠26周及之前，AUROC为0.514，之后为0.469。在误覆盖率α=0.10时，边际覆盖率为0.897，弃权（不执行）率为72.7%，单例预测准确率为0.624。这些结果为EHG预测建立了记录分离的参考基准，并为分段生理数据确立了更广泛的验证原则：重抽样单位应对应于预测性能预期泛化的单位；保形预测进一步量化了可用信息支持单例分类的情况，以及不确定性要求推迟决策的情况。
英文摘要：
Preterm birth remains a major cause of neonatal morbidity and mortality worldwide. Electrohysterography (EHG), a noninvasive measure of uterine myoelectrical activity, has been studied for preterm-birth prediction, but performance estimates may be biased when segments from the same maternal record are split across training and validation data. We formalized the distinction between segment-level and patient-independent record-grouped validation, established a patient-independent benchmark on the Term-Preterm Electrohysterogram Database, and evaluated class-conditional conformal selective prediction. All 300 records (38 preterm) were analyzed across three prespecified regimes. A 92-feature elastic-net logistic model was evaluated with record-grouped nested cross-validation; preprocessing, model fitting, Platt calibration, and conformal estimation were confined to training data, and performance was calculated from strictly out-of-fold record-level predictions with 1,000 bootstrap resamples. Under patient-independent evaluation, AUROC was 0.493 (95% CI 0.467-0.520), AUPRC 0.122 (0.095-0.152), and Brier score 0.115 (0.094-0.137). AUROC was 0.514 at or before 26 weeks and 0.469 thereafter. At miscoverage alpha=0.10, marginal coverage was 0.897, abstention 72.7%, and singleton-prediction accuracy 0.624. These findings establish a record-separated reference benchmark for EHG prediction and a broader validation principle for segmented physiological data: the unit of resampling should correspond to the unit at which predictive performance is intended to generalize. Conformal prediction further quantifies when the available information supports a singleton classification and when uncertainty warrants deferral.
