De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation.
De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation.
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DOI：
10.1186/s12911-023-02176-6
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发表时间：
2023-05-05
影响因子：
3.5
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中科院分区：
文献类型：
--
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流行病学研究可能需要将来自多个组织的信息联系起来。这可能带来两个问题：（1）信息治理需要在不共享直接标识符的情况下进行链接，以及（2）需要在没有共同的个人唯一标识符的情况下链接数据库。我们开发了贝叶斯匹配技术来解决这两个问题。我们提供了一个开源的软件实现，能够去识别的概率匹配，尽管差异，通过模糊表示和完全不匹配，加上去识别的确定性匹配，如果需要的话。我们通过测试英国国家卫生服务信托基金中多个医疗记录系统之间的联系来验证该技术，研究决策阈值对联系准确性的影响。我们报告了与正确联系相关的人口因素。该系统支持出生日期（DOB），名字，姓氏，三个州的性别和英国邮政编码。除性别外，其他所有类别都支持模糊表示，并且支持其他转换，例如口音错误表示、多部分姓氏的变化和名称重新排序。计算的对数比值预测了先证者在样本数据库中的存在，非自身数据库比较的受试者工作曲线下面积为0.997 - 0.999。通过考虑阈值θ和领先优势阈值δ将对数赔率转换为决策。选择了错误识别与连锁失败相比的20倍惩罚。默认情况下，为了计算效率，不允许完全的DOB不匹配。在这些设置下，对于非自身数据库比较，先证者被正确声明为样本中的平均概率为0.965（范围0.931 - 0.994），错误识别率为0.00249（范围0.00123 - 0.00429）。正确的联系与男性性别，黑人或混合种族，以及严重精神疾病或其他精神障碍的诊断代码的存在呈正相关，与出生年份，未知种族，居住区剥夺和伪邮政编码的存在呈负相关（例如，表明无家可归）。如果在软件的支持下也使用个人独有的识别资料，准确率将进一步提高。我们两个最大的数据库通过解释型编程语言在44分钟内连接起来。在没有个人唯一识别码的情况下，完全去识别的高精度匹配是可行的，适当的软件是免费提供的。在线版本包含补充材料，可通过10.1186/s12911 - 023 - 02176 - 6获得。
Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier. We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage. The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband’s presence in the sample database with an area under the receiver operating curve of 0.997–0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ. Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931–0.994), and the misidentification rate was 0.00249 (range 0.00123–0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language. Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available. The online version contains supplementary material available at 10.1186/s12911-023-02176-6.
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影响因子：
2.1
作者：
Bian, Jiang;Loiacono, Alexander;Hogan, William
通讯作者：
Hogan, William
影响因子：
3.5
作者：
Finney, John M.;Walker, A. Sarah;Wyllie, David H.
通讯作者：
Wyllie, David H.
影响因子：
7.2
作者：
Blake, Helen A.;Sharples, Linda D.;Walker, Kate
通讯作者：
Walker, Kate
影响因子：
--
作者：
Brown, A P;Randall, S M;Ferrante, A M
通讯作者：
Ferrante, A M
影响因子：
2.9
作者：
Downs, Johnny M.;Ford, Tamsin;Hayes, Richard
通讯作者：
Hayes, Richard