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A Multiagent System for Dynamic Data Aggregation in Medical Research.

Alevtina Dubovitskaya¹, Visara Urovi², Imanol Barba³

¹Applied Intelligent Systems Laboratory, HES-SO VS, Sierre, Switzerland; Distributed Information Systems Laboratory, EPFL, Lausanne, Switzerland.

Biomed Research International

|December 16, 2016

Summary

This summary is machine-generated.

Collecting medical data for research is slow. This new framework uses agent-based coordination and peer-to-peer networks to dynamically aggregate data from distributed sources, making medical research data collection more efficient.

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Area of Science:

Biomedical informatics
Data science
Health services research

Background:

Medical data collection for research is a complex and time-consuming challenge.
Existing methods often struggle with efficiency and scalability.

Purpose of the Study:

To propose a novel framework for the dynamic aggregation of medical data from distributed sources.
To accelerate and facilitate the process of collecting medical data for research.

Main Methods:

Utilizing agent-based coordination between medical and research institutions.
Employing peer-to-peer network principles for searching distributed databases.
Identifying potential data contributors for new database construction.

Main Results:

The framework dynamically aggregates medical data from distributed sources.
It considers research study requirements and data availability for better database definition (schema, content, privacy).
Demonstrates a more efficient approach to medical data collection.

Conclusions:

The proposed framework significantly improves the efficiency of medical data collection for research.
Agent-based coordination and peer-to-peer networks are effective for distributed data aggregation.
This approach optimizes database characteristics based on research needs and data availability.