Time series subsequence matching has importance in a variety of areas in healthcare informatics. These include case-based diagnosis and treatment as well as discovery of trends among patients. However, few medical systems employ subsequence matching due to high computational and memory complexities. This paper proposes a randomized Monte Carlo sampling method to broaden search criteria with minimal increases in computational and memory complexities over $R$-NN indexing. Information gain improves while producing result sets that approximate the theoretical result space, query results increase by several orders of magnitude, and recall is improved with no significant degradation to precision over $R$-NN matching.

时间序列子序列匹配在医疗保健信息学的各个领域中都很重要。这些包括基于案例的诊断和治疗以及发现患者趋势。但是，由于高度的计算和存储复杂性，很少有医疗系统采用子序列匹配。本文提出了一种随机蒙特卡洛采样方法，以在不增加计算和存储复杂性的情况下扩大搜索标准$\mathrm{[R}$-NN索引。在生成近似于理论结果空间的结果集的同时，信息增益得到改善，查询结果增加了几个数量级，查全率得到了改善，而精度没有明显下降。$\mathrm{[R}$-NN匹配。