融合静态与时间序列数据，高效计算患者相似性以提升临床决策支持
Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data
- 采用aWOE和Z-score转换静态数据，结合DTW处理时间序列，提升模型性能
- 在冠心病和心衰预测中，AUC、准确率、F值分别提升最高12.6%、21.9%
- 分布式计算使运行时间减少40%，适合大规模医疗数据应用
患者相似性计算（PSC）是医疗信息学中的核心问题，旨在根据历史临床记录衡量患者间相似性，以辅助临床决策。本文提出一种基于数据转换（DT）的分布式患者相似性计算（DPSC）方法，有效融合传感器采集的时间序列数据（如心率、血压、血氧饱和度等）与患者背景静态数据（如年龄、体重、性别等）。静态数据经自适应证据权重（aWOE）和Z-score变换后输入模型，既提升预测性能又保护隐私。时间序列相似性采用动态时间规整（DTW），针对其计算开销大问题，引入分布式实现。在冠状动脉疾病预测中，该方法使AUC、准确率、F-measure分别提升11.4%、10.20%、12.6%；在充血性心力衰竭（CHF）中，相应提升达15.9%、10.5%、21.9%。整体计算时间缩短最高达40%。
原文摘要 · Abstract (English)
Patient similarity computation (PSC) is a fundamental problem in healthcare informatics. The aim of the patient similarity computation is to measure the similarity among patients according to their historical clinical records, which helps to improve clinical decision support. This paper presents a novel distributed patient similarity computation (DPSC) technique based on data transformation (DT) methods, utilizing an effective combination of time series and static data. Time series data are sensor-collected patients' information, including metrics like heart rate, blood pressure, Oxygen saturation, respiration, etc. The static data are mainly patient background and demographic data, including age, weight, height, gender, etc. Static data has been used for clustering the patients. Before feeding the static data to the machine learning model adaptive Weight-of-Evidence (aWOE) and Z-score data transformation (DT) methods have been performed, which improve the prediction performances. In aWOE-based patient similarity models, sensitive patient information has been processed using aWOE which preserves the data privacy of the trained models. We used the Dynamic Time Warping (DTW) approach, which is robust and very popular, for time series similarity. However, DTW is not suitable for big data due to the significant computational run-time. To overcome this problem, distributed DTW computation is used in this study. For Coronary Artery Disease, our DT based approach boosts prediction performance by as much as 11.4%, 10.20%, and 12.6% in terms of AUC, accuracy, and F-measure, respectively. In the case of Congestive Heart Failure (CHF), our proposed method achieves performance enhancement up to 15.9%, 10.5%, and 21.9% for the same measures, respectively. The proposed method reduces the computation time by as high as 40%.
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