Clinical Data Analytics

Clinical Data Analytics is the process of extracting actionable insights from healthcare data to support evidence-based decision-making, improve patient outcomes, and optimize clinical workflows. This approach enables healthcare providers to identify trends, patterns, and opportunities for improvement.

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Why Actionable Insights and Reporting Frameworks are Urgently Needed: Part I

Apr 17, 2023

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The 8 Benefits of Optimized SaaS in Healthcare - And How to Obtain Them Using Best Practices

May 12, 2023

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Insights on AI and Care Pathway Optimization Adoption

May 16, 2023

"Big genomics and clinical data analytics strategies for precision cancer prognosis"

Researchers Ghim Siong Ow and Vladimir A. Kuznetsov have optimized their Prognostic Signature Vector Matching (PSVM) model for patient classification. They found that using negative log-rank p-values improves the model's robustness. They also combined PSVM with other machine learning techniques in a multi-test system for more precise patient stratification.

"Joint imaging platform for Federated Clinical Data Analytics"

Researchers Jonas Scherer, Marco Nolden, Jens Kleesiek, Jasmin Metzger, Klaus Kades, Verena Schneider, Hanno Gao, Peter Neher, Ralf Floca, Heinz-Peter Schlemmer, and Klaus Maier-Hein discuss the potential of AI in healthcare, particularly in image analysis for disease prediction, diagnosis, and treatment. They address the challenges of data sharing between institutions and propose the Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) as a solution for secure and compliant federated data analysis.

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