Multi-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics

Welten, Sascha; Hempel, Lars; Abedi, Masoud; Mou, Yongli; Jaberansary, Mehrshad; Neumann, Laurenz; Weber, Sven; Tahar, Kais; Yediel, Yeliz Ucer; Löbe, Matthias; Decker, Stefan; Beyan, Oya; Kirsten, Toralf

doi:10.3390/app12094336

Cited by 8 publications

(14 citation statements)

References 30 publications

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“…The user-study used two distributed data sources with synthetic data, while ISIC-GEN used three data sources with synthetic data, and ISIC-SAMPLE used one data source with real sample data ( 13 ). Lastly, the BC use case leveraged six data sources with real sample data ( 14 ).…”

Section: Resultsmentioning

confidence: 99%

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Welten,

Weber,

Holt

et al. 2024

Front. Med.

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The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution. Failing to detect such SPOFs can, for example, result in improper termination of the DA code, necessitating additional efforts from multiple stakeholders to resolve the malfunctions. Moreover, these malfunctions disrupt the seamless conduct of DA and entail several crucial consequences, including technical obstacles to resolve the issues, potential delays in research outcomes, and increased costs. In this study, we address this challenge by introducing a concept based on a method called Smoke Testing, an initial and foundational test run to ensure the operability of the analysis code. We review existing DA platforms and systematically extract six specific Smoke Testing criteria for DA applications. With these criteria in mind, we create an interactive environment called Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR), which allows researchers to perform Smoke Tests on their DA experiments. We conduct a user-study with 29 participants to assess our environment and additionally apply it to three real use cases. The results of our evaluation validate its effectiveness, revealing that 96.6% of the analyses created and (Smoke) tested by participants using our approach successfully terminated without any errors. Thus, by incorporating Smoke Testing as a fundamental method, our approach helps identify potential malfunctions early in the development process, ensuring smoother data-driven research within the scope of DA. Through its flexibility and adaptability to diverse real use cases, our solution enables more robust and efficient development of DA experiments, which contributes to their reliability.

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Section: Resultsmentioning

confidence: 99%

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Welten,

Weber,

Holt

et al. 2024

Front. Med.

Self Cite

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“…10 Sollis et al ( 2023) presents the NHGRI-EBI GWAS catalog, which was implemented as a knowledge base and is available as FAIR resource. Among these studies, Welten et al (2022); Deist et al (2020); Sinaci et al (2023) also address issues related to data security and privacy.…”

Section: Resultsmentioning

confidence: 99%

Data Management Plan for Healthcare: Following FAIR Principles and Addressing Cybersecurity Aspects. A Systematic Review using InstructGPT

Stanciu

2023

Preprint

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This study focuses on data-related aspects and emphasizes the importance of producing a data management plan (DMP) to address the challenges specific to the collection, processing, storage, security, documentation, sharing, and distribution of data in research projects in the healthcare sector. It provides an overview of the DMP and offers guidelines for creating an effective plan that incorporates the FAIR principles. Additionally, the study outlines the main aspects of data management in the healthcare domain and analyzes several security issues, such as cryptography, biometrics, and digital watermarking, that should be considered for healthcare data. A systematic review of the literature is performed to explore the critical aspects of data management and identify emerging trends, challenges, and innovative solutions that can be incorporated into DMPs.

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“…The generated reference list can be downloaded from the GitHub repository. 35 To enable cross-site reporting on DQ, a distributed DQ analysis was implemented using PHT 36,37 . PHT is an infrastructure to support distributed data analytics of medical health data, while the data remains under the control of the data holders.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Each hospital set up a single PHT station as well as a FHIR server for synthetic RD patients. We used the station software and On-Boarding workflow 37 to set up the required IT infrastructure for PHT. We also implemented the algorithms as a Docker image to run the DQ analysis in a distributed way as mentioned in section 3.5.…”

Section: Experiments Settings For Distributed Dq Assessmentsmentioning

confidence: 99%

“…37 To enable cross-site reporting on DQ, a distributed DQ analysis was implemented using PHT. 43,44 PHT is an infrastructure to support distributed data analytics of medical health data, while the data remain under the control of the data holders. There are two main concepts in PHT, i.e., station and train.…”

Section: Implementation Of the Software Framework And Data Quality As...mentioning

confidence: 99%

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Rare Diseases in Hospital Information Systems—An Interoperable Methodology for Distributed Data Quality Assessments

et al. 2023

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Background: Multisite research networks such as the project “Collaboration on Rare Diseases” connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. Objectives: The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. Methods: We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. Results: Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. Conclusion: We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.

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Multi-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics

Cited by 8 publications

References 30 publications

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Data Management Plan for Healthcare: Following FAIR Principles and Addressing Cybersecurity Aspects. A Systematic Review using InstructGPT

Rare Diseases in Hospital Information Systems—An Interoperable Methodology for Distributed Data Quality Assessments

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