Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Area of Science:

• Biomedical informatics
• Artificial intelligence in healthcare
• Machine learning applications

Background:

• Deep learning (DL) has gained prominence for analyzing complex biomedical data.
• Healthcare generates vast amounts of data (big data) from various sources.
• Mobile health (mHealth) is a key driver for personalized healthcare delivery.

Purpose of the Study:

• To review the fundamentals and trends of DL methods in healthcare.
• To highlight DL implementations across different biomedical domains.
• To discuss challenges and future research directions for DL in health management.

Main Methods:

• Literature review of DL applications in healthcare.
• Data sourced from PubMed and IEEE Xplore databases.
• Categorization of DL implementations into biological systems, electronic health records, medical imaging, and physiological signals.

Main Results:

• DL demonstrates significant achievements in feature extraction and complex task accomplishment.
• DL effectively analyzes big data generated from mHealth applications.
• Key application areas include medical image analysis, EHR processing, and physiological signal interpretation.

Conclusions:

• DL is crucial for leveraging big data in personalized healthcare.
• Addressing DL challenges is vital for advancing biomedical and health domains.
• Future research should focus on integrating DL with physiological signals and internet technology for enhanced health management.