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Clinical Trials

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...

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Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study.

Ashkan Pirmani^1,2,3,4, Martijn Oldenhof¹, Liesbet M Peeters^2,3,4

¹ESAT-STADIUS, KU Leuven, Leuven, Belgium.

JMIR Formative Research

|July 17, 2024

Summary

Federated Learning for Everyone (FL4E) simplifies collaborative clinical research by enabling flexible data sharing. This framework enhances data analysis and collaboration without the overhead of fully federated models.

Keywords:

accessible clinical research design effectiveness ecosystem federated learning implementation inclusive inclusivity integrity multistakeholder collaboration real-world data reliability

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Area of Science:

* Clinical research and data science.
* Health informatics and collaborative platforms.

Background:

* Clinical research relies on vast datasets, but data fragmentation and access barriers hinder progress.
* Federated learning (FL) offers a solution for leveraging distributed data but faces implementation, scalability, and inclusivity challenges.

Purpose of the Study:

* Introduce Federated Learning for Everyone (FL4E), an accessible framework for multistakeholder clinical research collaboration.
* Simplify federated learning adoption through an ecosystem-based approach and customizable data decentralization.

Main Methods:

* Introduce the

Main Results:

* FL4E's ecosystem-oriented and inclusive design was validated using real-world healthcare data.
* Hybrid models achieved performance comparable to fully federated models while reducing overhead.
* The

Conclusions:

* FL4E merges centralized and federated learning, offering an inclusive and customizable framework for clinical research.
* The