PARSE: A personalized clinical time-series representation learning framework via abnormal offsets analysis
PARSE: A personalized clinical time-series representation learning framework via abnormal offsets analysis
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DOI：
10.1016/j.cmpb.2023.107838
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发表时间：
2023-10-11
影响因子：
6.1
通讯作者：
Guo，Lin
中科院分区：
文献类型：
--
作者：
An，Ying;Cai，Guanglei;Guo，Lin
背景与目的：患者临床风险预测是卫生保健领域的一个重要研究课题，对疾病的诊断、治疗和预防具有重要意义。近年来，人们提出了大量基于深度学习的方法，通过从历史电子健康记录（Electronic health Records, EHRs）数据中挖掘患者健康状况的相关特征来进行临床预测。然而，现有的这些方法大多只关注于发现实验室检测、体检等生理指标的时间序列特征，而没有综合考虑这些生理指标偏离正常范围的程度及其稳定性，从而极大地限制了预测效果。方法：提出一种基于异常偏移量分析的个性化临床时间序列表征学习框架PARSE，用于临床风险预测。在PARSE中，我们在从原始EHR数据中提取相关时间特征的同时，进一步从各生理指标观测值相对于正常值的绝对偏移量和相邻两个时间步上各生理指标观测值的相对偏移量中获取异常状态的相关特征作为补充信息。最后，引入自适应融合模块，有效整合上述特征，获得个性化患者表征，用于临床风险预测。结果：我们在两个公开的真实世界数据集上进行了住院死亡率预测任务。PARSE获得了最高的F1分数，分别为48.1%和40.3%，在两个数据集上分别提高了2.4%和6.2%，优于最先进的方法。此外，烧蚀实验结果表明，这两种异常偏移和自适应融合方法是有贡献的。结论：PARSE可以更好地从电子病历数据中提取风险相关信息，提高患者陈述的个性化。PARSE的每个部分都独立地提高了最终的预测性能。
Background and objective: Clinical risk prediction of patients is an important research issue in the field of healthcare, which is of great significance for the diagnosis, treatment and prevention of diseases. In recent years, a large number of deep learning-based methods have been proposed for clinical prediction by mining relevant features of patients' health condition from historical Electronic Health Records (EHRs) data. However, most of these existing methods only focus on discovering the time series characteristics of physiological indexes such as laboratory tests and physical examinations, and fail to comprehensively consider the deviation degree of these physiological indexes from the normal range and their stability, thus greatly limiting the prediction performance.Methods: We propose a personalized clinical time-series representation learning framework via abnormal offsets analysis named PARSE for clinical risk prediction. In PARSE, while extracting relevant temporal features from the original EHR data, we further capture relevant features of abnormal condition as complementary information from the absolute offset of each physiological index's observed values from its normal value and the relative offset between each physiological index's observed values in two adjacent time steps. Finally, an adaptive fusion module is introduced to effectively integrate the above features to obtain the personalized patient's representations for clinical risk prediction.Results: We conduct an in-hospital mortality prediction task on two public real-world datasets. PARSE achieves the highest F1 scores of 48.1% and 40.3%, outperforming the state-of-the-art methods with a boost of 2.4% and 6.2% on two datasets respectively. Furthermore, the results of ablation experiments demonstrate that the two abnormal offsets and the proposed adaptive fusion method are contributing.Conclusions: PARSE can better extract the risk-related information from the EHRs data and improve the personalization of the patients' representations. Each part of PARSE improves the final prediction performance independently.