用GRU-D分析MIMIC-IV中年龄相关的生命体征缺失模式。
GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV
- 基于衰减门控机制的GRU-D模型捕捉时间序列缺失规律。
- 在bootstrap数据上达到0.780 AUROC和0.810 AUPRC。
- 揭示血压与呼吸频率缺失差异是关键预测信号，适合临床时序建模研究者。
时间缺失指时间序列中未观测到的模式，其预测潜力是临床机器学习中的新兴方向。我们训练了一种带有衰减机制的门控循环单元（GRU-D），用于区分老年与年轻患者。以MIMIC-IV中5项生命体征的时间序列作为输入。在自助采样数据上，GRU-D的AUROC为0.780，AUPRC为0.810。通过解析训练后的模型参数，发现血压缺失和呼吸频率缺失的差异是参数化隐藏门单元所学习的重要预测因子。结果表明，GRU-D可有效揭示时间缺失模式，为新研究方向奠定基础。
原文摘要 · Abstract (English)
Temporal missingness, defined as unobserved patterns in time series, and its predictive potentials represent an emerging area in clinical machine learning. We trained a gated recurrent unit with decay mechanisms, called GRU-D, for a binary classification between elderly - and young patients. We extracted time series for 5 vital signs from MIMIC-IV as model inputs. GRU-D was evaluated with means of 0.780 AUROC and 0.810 AUPRC on bootstrapped data. Interpreting trained model parameters, we found differences in blood pressure missingness and respiratory rate missingness as important predictors learned by parameterized hidden gated units. We successfully showed how GRU-D can be used to reveal patterns in temporal missingness building the basis of novel research directions.
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