IEEE Journal of Biomedical and Health Informatics

Scope & Guideline

Exploring New Frontiers in Health and Biomedical Technology

Introduction

Explore the comprehensive scope of IEEE Journal of Biomedical and Health Informatics through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore IEEE Journal of Biomedical and Health Informatics in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN2168-2194
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2013 to 2024
AbbreviationIEEE J BIOMED HEALTH / IEEE J. Biomed. Health Inform.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address445 HOES LANE, PISCATAWAY, NJ 08855-4141

Aims and Scopes

The IEEE Journal of Biomedical and Health Informatics aims to advance the field of biomedical and health informatics by publishing innovative research that integrates data science, machine learning, and health technologies. This journal emphasizes the application of computational methods to improve healthcare delivery, enhance clinical decision-making, and facilitate the management of health data.
  1. Biomedical Data Analysis and Machine Learning:
    The journal focuses on the application of machine learning techniques to analyze biomedical data, including genomics, imaging, and electronic health records. It promotes research that develops algorithms for predictive modeling, classification, and risk assessment in healthcare.
  2. Health Monitoring Technologies:
    Research in this area includes the development and validation of wearable and remote monitoring devices that track physiological parameters. The journal highlights studies that utilize these technologies for chronic disease management and real-time health monitoring.
  3. Medical Imaging and Image Processing:
    A core area of the journal is the application of deep learning and other computational techniques to enhance medical imaging. This includes image segmentation, classification, and analysis to support diagnosis and treatment planning.
  4. Health Informatics and Decision Support Systems:
    The journal publishes research that integrates health informatics with clinical workflows, emphasizing the development of decision support systems that aid healthcare professionals in making evidence-based decisions.
  5. Privacy and Security in Health Data:
    Given the sensitive nature of health data, the journal addresses methodologies that ensure privacy and security in the sharing and analysis of health information, particularly in the context of IoT and federated learning.
  6. Patient-Centric Approaches and Digital Health:
    Research that emphasizes user engagement, personalized medicine, and patient-centric health solutions is a significant focus. The journal encourages studies that explore the intersection of technology and patient care.
The IEEE Journal of Biomedical and Health Informatics is witnessing significant growth in several research areas that align with current technological advancements and healthcare needs. These emerging themes reflect the journal's adaptability to the evolving landscape of biomedical informatics.
  1. Artificial Intelligence in Healthcare:
    AI continues to dominate the research landscape, with a surge in studies applying deep learning and machine learning techniques to improve diagnostics, treatment outcomes, and personalized medicine.
  2. Wearable Health Technology and Remote Monitoring:
    There is an emerging trend in research focusing on wearable devices and remote health monitoring solutions. This includes advancements in sensor technology and algorithms for real-time data processing, which are critical for managing chronic diseases.
  3. Federated Learning and Privacy-Preserving Techniques:
    With increasing concerns over data privacy, there is a growing focus on federated learning approaches that allow collaborative model training without sharing sensitive health data. This trend is vital for enhancing the security of health informatics.
  4. Multimodal Data Integration:
    The integration of multimodal data sources, such as combining imaging, genomic, and clinical data, is gaining traction. Researchers are exploring how these diverse datasets can improve predictive modeling and patient outcomes.
  5. Telehealth and Digital Therapeutics:
    The COVID-19 pandemic has accelerated the development of telehealth solutions and digital therapeutics, leading to an increase in research focused on remote patient care, virtual consultations, and digital health interventions.
  6. Explainable AI in Medical Applications:
    As AI becomes more prevalent in healthcare, there is a rising emphasis on explainability and transparency in AI models. Researchers are developing methods to ensure that AI-driven decisions can be understood and trusted by clinicians.

Declining or Waning

While the journal continues to thrive in many areas, certain themes appear to be declining in prominence. These waning scopes reflect shifts in research trends and technological advancements that may influence future publication topics.
  1. Traditional Statistical Methods in Health Research:
    There seems to be a reduced focus on conventional statistical approaches in favor of more advanced machine learning and artificial intelligence techniques. This shift indicates a growing preference for complex models that can handle high-dimensional data and provide deeper insights.
  2. Basic Health Technology Assessments:
    Research that simply evaluates the efficacy of health technologies without integrating advanced computational methods or data analytics appears less frequently. The focus has shifted towards more sophisticated analyses that combine multiple data sources and advanced algorithms.
  3. Single-Modal Data Approaches:
    The use of single-modality data for health assessments is becoming less common, as there is a growing emphasis on multimodal data integration. Studies that solely rely on one type of data source, such as imaging or clinical records, are being overshadowed by research that combines various modalities.
  4. General Health Informatics without Specific Applications:
    There is a noticeable decline in publications that discuss health informatics in broad terms without a specific application or case study. The journal is increasingly favoring research that demonstrates practical implementations and real-world impacts.

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