Facilitating data collaboration for precision medicine.

What is it about?

Precision medicine promises more effective disease treatment by stratification and personalization of diagnosis and treatment. Further disease stratification requires more data from more patients and the use of Artificial Intelligence (AI). Traditional Medical Data Management Systems (MedDMSs) are not designed for implementation of precision medicine. We defined, built and applied a MedDMS for precision medicine. A MedDMS for precision medicine would (a) be compliant to the GDPR and other privacy protection guidelines, (b) facilitate multi-center data collaboration in research consortia, (c) allow sharing of existing and new data, (d) provide data ready for machine learning AI to develop predictive algorithms, and (e) allow the data to be used in care settings with clinical decision support tools. We developed LogiqSuite, a MedDMS compliant with these demands. We have selected use-cases from different biomedical fields, like oncology, pulmonology, cardiovascular risk management, and prehospital triage. We used LogiqSuite in multi-centre data registrations and monitoring, data analytics, for AI to develop algorithms for prediction models, to integrate of care with advanced medicine production, and for real-world data monitoring.

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Photo by Jan Antonin Kolar on Unsplash

Photo by Jan Antonin Kolar on Unsplash

Why is it important?

Precision medicine offers new and more effective treatments by stratification and personalization of medicine, increasing its data complexity. It uses more and more complex data than regular medicine, with different sensitivities, and should facilitate automated data analysis. The paper describes a data management system for medical data and provides examples of its use in various settings for precision medicine. The transition to precision medicine depends on appropriate tools for automated collection, multilayered data authorization access and real-time preprogrammed and machine learning data science.

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