Classification-Based Record Linkage With Pseudonymized Data for Epidemiological Cancer Registries

Siegert, Yannik; Jiang, Xiaoyi; Krieg, V.; Bartholomäus, Sebastian

doi:10.1109/tmm.2016.2598482

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…These uncertain matches need to be manually resolved. To approach this issue, we presently examine a set of machine learning techniques [15]. We trained the classifiers with routine decision data of the LKR-NRW and the results appear to indicate a reduction of manual work by 80 % without sacrificing quality.…”

Section: Discussionmentioning

confidence: 99%

Secure Linking of Data from Population-Based Cancer Registries with Healthcare Data to Evaluate Screening Programs

Bartholomäus¹,

Siegert²,

Hense

et al. 2019

Gesundheitswesen

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Background The evaluation of population-based screening programs, like the German Mammography Screening Program (MSP), requires collection and linking data from population-based cancer registries and other sources of the healthcare system on a case- specific level. To link such sensitive data, we developed a method that is compliant with German data protection regulations and does not require written individual consent. Methods Our method combines a probabilistic record linkage on encrypted identifying data with ‘blinded anonymisation’. It ensures that all data either are encrypted or have a defined and measurable degree of anonymity. The data sources use a software to transform plain-text identifying data into a set of irreversibly encrypted person cryptograms, while the evaluation attributes are aggregated in multiple stages and are reversibly encrypted. A pseudonymisation service encrypts the person cryptograms into record assignment numbers and a downstream data-collecting centre uses them to perform the probabilistic record linkage. The blinded anonymisation solves the problem of quasi-identifiers within the evaluation data. It allows selecting a specific set of the encrypted aggregations to produce data export with ensured k-anonymity, without any plain-text information. These data are finally transferred to an evaluation centre where they are decrypted and analysed. Our approach allows creating several such generalisations, with different resulting suppression rates allowing dynamic balance information depth with privacy protection and also highlights how this affects data analysability. Results German data protection authorities approved our concept for the evaluation of the impact of the German MSP on breast cancer mortality. We implemented a prototype and tested it with 1.5 million simulated records, containing realistically distributed identifying data, calculated different generalisations and the respective suppression rates. Here, we also discuss limitations for large data sets in the cancer registry domain, as well as approaches for further improvements like l-diversity and how to reduce the amount of manual post-processing. Conclusion Our approach enables secure linking of data from population-based cancer registries and other sources of the healthcare system. Despite some limitations, it enables evaluation of the German MSP program and can be generalised to be applicable to other projects.

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Section: Discussionmentioning

confidence: 99%

Secure Linking of Data from Population-Based Cancer Registries with Healthcare Data to Evaluate Screening Programs

Bartholomäus¹,

Siegert²,

Hense

et al. 2019

Gesundheitswesen

Self Cite

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“…Regarding to the record linkage problem, there are multiple approaches based on machine learning. For example, some of them aim discover drugs [58] or relationships among medical records [59] [60]. But these kind of applications are domain-dependent [61], [62] and requires specific steps for concrete applications.…”

Section: Machine Learning and Medicinementioning

confidence: 99%

Automatic Learning Framework for Pharmaceutical Record Matching

et al. 2020

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Pharmaceutical manufacturers need to analyse a vast number of products in their daily activities. Many times, the same product can be registered several times by different systems using different attributes, and these companies require accurate and quality information regarding their products since these products are drugs. The central hypothesis of this research work is that machine learning can be applied to this domain to efficiently merge different data sources and match the records related to the same product. No human is able to do this in a reasonable way because the number of records to be matched is extremely high. This paper presents a framework for pharmaceutical record matching based on machine learning techniques in a big data environment. The proposed framework aims to explode the well-known rules for the matching of records from different databases for training machine learning models. Then the trained models are evaluated by predicting matches with records that do not follow these known rules. Finally, the production environment is simulated by generating a huge amount of combinations of records and predicting the matches. The obtained results show that, despite the good results obtained with the training datasets, in the production environment, the average accuracy of the best model is around 85%. That shows that matches which do not follow the known rules can be predicted and, considering that there is not a human way to process this amount of data, the results are promising.

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“…A singular case is the work Siegert et al (2016) in the linkage of epidemiological cancer registries data previously pseudo-randomized through hashing and encrypted for privacy reasons. Features are extracted from the obscured data and used as they were a new coding of the records, then the classification is performed on these coded data.…”

Section: Related Workmentioning

confidence: 99%

Record linkage of banks and municipalities through multiple criteria and neural networks

Maratea

Ciaramella

Cianci

2020

PeerJ Computer Science

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Record linkage aims to identify records from multiple data sources that refer to the same entity of the real world. It is a well known data quality process studied since the second half of the last century, with an established pipeline and a rich literature of case studies mainly covering census, administrative or health domains. In this paper, a method to recognize matching records from real municipalities and banks through multiple similarity criteria and a Neural Network classifier is proposed: starting from a labeled subset of the available data, first several similarity measures are combined and weighted to build a feature vector, then a Multi-Layer Perceptron (MLP) network is trained and tested to find matching pairs. For validation, seven real datasets have been used (three from banks and four from municipalities), purposely chosen in the same geographical area to increase the probability of matches. The training only involved two municipalities, while testing involved all sources (municipalities vs. municipalities, banks vs banks and and municipalities vs. banks). The proposed method scored remarkable results in terms of both precision and recall, clearly outperforming threshold-based competitors.

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Classification-Based Record Linkage With Pseudonymized Data for Epidemiological Cancer Registries

Cited by 11 publications

References 18 publications

Secure Linking of Data from Population-Based Cancer Registries with Healthcare Data to Evaluate Screening Programs

Secure Linking of Data from Population-Based Cancer Registries with Healthcare Data to Evaluate Screening Programs

Automatic Learning Framework for Pharmaceutical Record Matching

Record linkage of banks and municipalities through multiple criteria and neural networks

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