m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

Area of Science:

• Healthcare Technology
• Data Science
• Artificial Intelligence

Background:

• Mobile health (m-Health) is a significant technological advancement in modern healthcare.
• Big data analytics is a key driver for intelligent healthcare delivery systems.
• The convergence of big data and m-Health presents unique, largely untackled challenges.

Purpose of the Study:

• To identify and discuss big data challenges within the technological components of m-Health (communications, sensors, computing) and m-Health 2.0.
• To explore the integration of big m-Health data analytics with m-Health systems.
• To examine the current and future roles of machine learning and deep learning in smartphone-centric m-Health.

Main Methods:

• Review and analysis of technological building blocks in m-Health concerning big data.
• Exploration of data analytics, machine learning, and deep learning applications in m-Health.
• Discussion of stakeholder perspectives on the balance between m-Health innovation and data risks.

Main Results:

• Identified key big data issues across m-Health technological domains.
• Highlighted the potential of machine and deep learning for advancing m-Health analytics.
• Emphasized the need for stakeholder alignment to navigate the complexities of big data in m-Health.

Conclusions:

• Addressing big data challenges is crucial for realizing the full potential of intelligent m-Health.
• Machine learning and deep learning offer promising avenues for sophisticated m-Health data analysis.
• Future research must focus on balancing innovation with the risks and complexities of big data in connected healthcare systems.