Improving Biomedical Signal Search Results in Big Data Case-Based Reasoning Environments.
Improving Biomedical Signal Search Results in Big Data Case-Based Reasoning Environments.
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DOI：
10.1016/j.pmcj.2015.09.006
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发表时间：
2016-06
影响因子：
4.3
通讯作者：
Sarrafzadeh M
中科院分区：
文献类型：
--
作者：
Woodbridge J;Mortazavi B;Bui AA;Sarrafzadeh M
时间序列的子序列匹配在医疗保健信息学的各个领域都具有重要意义。这些包括以病例为基础的诊断和治疗以及发现患者的趋势。然而，由于计算和存储的复杂性，很少有医疗系统使用子序列匹配。这篇手稿提出了一种随机蒙特卡罗抽样方法，以扩大搜索标准，与R-NN索引相比，计算和存储复杂性增加最少。在产生接近理论结果空间的结果集的同时，信息增益得到了提高，查询结果增加了几个数量级，召回率也得到了提高，而准确率与R-NN匹配相比没有明显下降。
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 manuscript 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 signi cant degradation to precision over R-NN matching.