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Rapid health data repository allocation using predictive machine learning.

Md Ashraf Uddin¹, Andrew Stranieri¹, Iqbal Gondal¹

¹Federation University Australia, Australia.

Health Informatics Journal

|September 24, 2020

Summary

This summary is machine-generated.

A new predictive model uses machine learning to intelligently select health data storage repositories in real-time. This system addresses the growing volume of digital health data, especially from wearable sensors, to meet patient needs.

Keywords:

Big Health data Blockchain classifier deep learning digital health record storage electronic health record machine learning quality of performance security and privacy stream data

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Area of Science:

Health Informatics
Machine Learning
Data Management

Background:

Diverse health data repositories exist, including Electronic Health Records, Electronic Medical Records, Personal Health Records, and Blockchain-based systems.
The proliferation of digital health data, driven by sources like wearable sensors, necessitates intelligent data storage solutions.
Current storage allocation decisions are complex, especially for continuously streamed data, and patient preferences play a role.

Purpose of the Study:

To propose a predictive model for real-time health data storage allocation.
To develop a system that accommodates patient needs and preferences in storage decisions.
To address the challenges of managing and storing continuously streamed health data from sources like wearable sensors.

Main Methods:

A machine learning classifier was employed to learn the relationship between health data characteristics and storage repository features.
A synthetic training dataset was generated based on expert correlations from small samples.
The model was designed for rapid, real-time decision-making, even with streaming data.

Main Results:

The evaluation demonstrated the viability and effectiveness of the proposed machine learning technique for health data storage allocation.
The predictive model shows promise in making efficient and personalized storage decisions.
The approach successfully handles the complexities of real-time data streaming.

Conclusions:

Machine learning offers a viable solution for intelligent health data storage management.
The developed model can efficiently allocate health data to appropriate repositories, considering patient needs.
This approach is crucial for managing the increasing volume and velocity of digital health data.