Register für seltene Erkrankungen

Storf, Holger; Schaaf, Jannik; Kadioglu, Dennis; Göbel, Jens; Wagner, Thomas; Ückert, Frank

doi:10.1007/s00103-017-2536-7

Cited by 17 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ADOPT BBMRI-ERIC project used an installation of the software Open Source Registry System for Rare Diseases (OSSE, Open-Source-Registersystem für Seltene Erkrankungen ) 20 as collection system for the CCDC data. OSSE is a case report form (CRF) system that generates forms for data entry by using standardized data element definitions from the Samply.MDR metadata repository (MDR).…”

Section: Methodsmentioning

confidence: 99%

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC

et al. 2019

View full text Add to dashboard Cite

Background High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. Objectives To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. Methods Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. Results The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. Conclusion A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.

show abstract

Section: Methodsmentioning

confidence: 99%

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Since its first release in 2013, Mainzelliste has been used by a constantly growing number of national medical research networks [6,7], centralized biobanks [8], research platforms [9], commercial data capture and analysis suites [10,11], registry software solutions [12,13] and patient organizations and related disease registries [14,15]. The software is under continuous development, incorporating community contributions from various research institutions [16].…”

Section: Mainzellistementioning

confidence: 99%

Optimization of the Mainzelliste software for fast privacy-preserving record linkage

et al. 2021

View full text Add to dashboard Cite

Background Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases. Methods We evaluate the linkage quality and performance of the linkage process using several real and near-real datasets with different properties w.r.t. size and error-rate of matching records. We conduct a comparison between (plaintext) record linkage and PPRL based on encoded records (Bloom filters). Furthermore, since the Mainzelliste software offers no blocking mechanism, we extend it by phonetic blocking as well as novel blocking schemes based on locality-sensitive hashing (LSH) to improve runtime for both standard and privacy-preserving record linkage. Results The Mainzelliste achieves high linkage quality for PPRL using field-level Bloom filters due to the use of an error-tolerant matching algorithm that can handle variances in names, in particular missing or transposed name compounds. However, due to the absence of blocking, the runtimes are unacceptable for real use cases with larger datasets. The newly implemented blocking approaches improve runtimes by orders of magnitude while retaining high linkage quality. Conclusion We conduct the first comprehensive evaluation of the record linkage facilities of the Mainzelliste software and extend it with blocking methods to improve its runtime. We observed a very high linkage quality for both plaintext as well as encoded data even in the presence of errors. The provided blocking methods provide order of magnitude improvements regarding runtime performance thus facilitating the use in research projects with large datasets and many participants.

show abstract

“…Reusable: Data can be used for further research using computational methods.. Further development of the OSSE registry framework is ongoing by the Medical Informatics Group (MIG) of the University Hospital Frankfurt. (21,(23)(24)(25) To improve timely and correct diagnosis for patients with rare diseases, the development of a 'registry for undiagnosed patients' was also recommended by the German National Action Plan, taking into account that a high percentage of these 'undiagnosed patients' eventually are diagnosed to have a rare disease. Due to the fact that undiagnosed patients present with a wide variety of symptoms at different levels and specialities within the health care system, it is even more complicated to assess the number of undiagnosed patients.…”

Section: Actions For Rare Diseasesmentioning

confidence: 99%

“…This in turn enables the researcher on the one hand to decide, which registries can provide appropriate data and on the other hand formulate a detailed inquiry for data using a so called OSSE decentral search inquiry. (25) Another strong point of the OSSE software is data protection. For pseudonymization, OSSE uses a broadly established open-source software, Mainzelliste, developed by the University Medical Centre of the Johannes Gutenberg University Mainz.…”

Section: Registry Softwarementioning

confidence: 99%

How to design a registry for undiagnosed patients in the framework of rare disease diagnosis – suggestions on software, data set and coding system

Berger

Rustemeier

Göbel

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain.Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software OSSE (Open-Source Registry System for Rare Diseases) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded.Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some patients.

show abstract

Register für seltene Erkrankungen

Cited by 17 publications

References 8 publications

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC

Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC

Optimization of the Mainzelliste software for fast privacy-preserving record linkage

How to design a registry for undiagnosed patients in the framework of rare disease diagnosis – suggestions on software, data set and coding system

Contact Info

Product

Resources

About