Transfer of Clinical Drug Data to a Research Infrastructure on OMOP – A FAIR Concept

Reinecke, Ines; Zoch, Michéle; Wilhelm, Markus; Sedlmayr, Martin; Bathelt, Franziska

doi:10.3233/shti210815

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…In recent years, there has been a rapid increase in the number of health databases that have transferred their data to OMOP CDM [13, 14]. In parallel, more and more open-source software to analyze and visualize such data has become available.…”

Section: Discussionmentioning

confidence: 99%

TrajectoryViz: Interactive visualization of treatment trajectories

Pajusalu,

Mooses,

Oja

et al. 2024

Preprint

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Background and objectives: With the proliferation of real-world or observational health data, there is increasing interest in studying treatment trajectories. The real-life treatment trajectories can be complex, and one has to simplify the patterns to draw any conclusions; however, oversimplification will cause the loss of essential details. Thus, the visualization challenge is to strike a balance between the two extremes. Methods: We have implemented the observation of treatment trajectories starting from cohort definitions in cooperation with medical specialists, data processing, and then generating the interactive visualizations and detailed data tables within an open-source R package as a Shiny dashboard. The created R package called TrajectoryViz (https://github.com/HealthInformaticsUT/TrajectoryViz) enables reproducible visual analysis and visual content generation for various data investigations. Results: We illustrate the use of the tool by assessing the sequence of events present within the data of cervical cancer prevention pathways, as well as the proportions of timely follow-up procedure events. Conclusion: Building a toolset to access, manage, and analyze observational health data enables more accessible visual analysis of complicated data, adding time dimension to otherwise simplified event sequences that make up trajectories.

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Section: Discussionmentioning

confidence: 99%

TrajectoryViz: Interactive visualization of treatment trajectories

Pajusalu,

Mooses,

Oja

et al. 2024

Preprint

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“…In this light, a CDM should also be designed to be FAIR, as follows: (i) “Findable” by providing a standardized way of organizing and representing healthcare data that can be easily shared and accessed by researchers across different organizations and countries; (ii) “Accessible” by providing open-source tools and documentation to support the use of the CDM in research; (iii) “Interoperable” by providing a common data model that can be used to integrate data from multiple sources and to support standardized analysis and research; and finally (iv) “Reusable” by providing a flexible and adaptable framework that can be used for a wide range of research questions and applications. Taken together, CDMs are designed to support the FAIR principles for scientific data management and stewardship and they have been widely adopted by the research community for their ability to promote open and collaborative research in medicine [ 27 ]. In particular, for clinical AI models, adhering to the FAIR principles is of the utmost importance, to allow for a transparent, trustworthy, and reliable development of tools that can help advance clinical research and practice in a responsible and ethical manner [ 28 ].…”

Section: Common Ground For Ai-based Clinical Guidelines Via Fair Comm...mentioning

confidence: 99%

Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes

Salybekov,

Wolfien,

Hahn

et al. 2024

Biomedicines

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The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields, including nephrology. The integration of various AI techniques in nephrology facilitates the prediction of the early detection, diagnosis, prognosis, and treatment of kidney disease. Nevertheless, recent reports have demonstrated that the majority of published clinical AI studies lack uniform AI reporting standards, which poses significant challenges in interpreting, replicating, and translating the studies into routine clinical use. In response to these issues, worldwide initiatives have created guidelines for publishing AI-related studies that outline the minimal necessary information that researchers should include. By following standardized reporting frameworks, researchers and clinicians can ensure the reproducibility, reliability, and ethical use of AI models. This will ultimately lead to improved research outcomes, enhanced clinical decision-making, and better patient management. This review article highlights the importance of adhering to AI reporting guidelines in medical research, with a focus on nephrology and urology, and clinical practice for advancing the field and optimizing patient care.

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“…Apart from the initial processing and filtering, the curation of clinical data also involves dedicated checks and data transformations, e.g., ensuring that the values fall within acceptable ranges (e.g., checking maximum and minimum age and body mass index values), resolving inconsistencies (e.g., different units or value encodings), and transforming the data to standard formats (e.g., OMOP [ 24 ], CDISC [ 25 ], ICD10/11 [ 26 ], SNOMED CT [ 27 ]). Beyond these curation steps, a minimum set of required complementary annotations should be made available for subsequent data analyses and dissemination.…”

Section: Tip 2: Ensure Data Quality Curation and Standardizationmentioning

confidence: 99%