A benchmark comparison of deterministic and probabilistic methods for defining manual review datasets in duplicate records reconciliation
A benchmark comparison of deterministic and probabilistic methods for defining manual review datasets in duplicate records reconciliation
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DOI：
10.1136/amiajnl-2013-001744
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发表时间：
2014-01-01
影响因子：
6.4
通讯作者：
Bernstam, Elmer V.
中科院分区：
文献类型：
--
作者：
Joffe, Erel;Byrne, Michael J.;Bernstam, Elmer V.
临床数据库需要准确的实体解析（ER）。一种方法是使用算法，将有问题的案例分配给人工审查。很少有研究比较了常见算法的性能这样一个任务。此外，以前的工作一直受到限制，缺乏客观的方法来设置算法参数。我们比较了常见的ER算法的性能：使用算法优化，而不是手动参数调整，并在两个阈值分类（匹配/手动审查/非匹配）以及单阈值（匹配/非匹配）。方法我们手动审查20 000随机选择的，潜在的重复记录对，以确定匹配（10 000训练集，10 000测试集）。我们评估了概率期望最大化，简单的确定性和模糊推理机（FIE）算法。我们使用粒子群优化算法参数为一个单一的和两个阈值。我们使用训练集进行了10次迭代优化，并报告了测试集的平均性能。与概率方法相比，FIE和简单确定性算法允许较低的人工审查集（FIE 1.9%，简单确定性2.5%，概率3.6%; p
Introduction Clinical databases require accurate entity resolution (ER). One approach is to use algorithms that assign questionable cases to manual review. Few studies have compared the performance of common algorithms for such a task. Furthermore, previous work has been limited by a lack of objective methods for setting algorithm parameters. We compared the performance of common ER algorithms: using algorithmic optimization, rather than manual parameter tuning, and on two-threshold classification (match/manual review/non-match) as well as single-threshold (match/non-match).Methods We manually reviewed 20 000 randomly selected, potential duplicate record-pairs to identify matches (10 000 training set, 10 000 test set). We evaluated the probabilistic expectation maximization, simple deterministic and fuzzy inference engine (FIE) algorithms. We used particle swarm to optimize algorithm parameters for a single and for two thresholds. We ran 10 iterations of optimization using the training set and report averaged performance against the test set.Results The overall estimated duplicate rate was 6%. FIE and simple deterministic algorithms allowed a lower manual review set compared to the probabilistic method (FIE 1.9%, simple deterministic 2.5%, probabilistic 3.6%; p