About this Research Topic
Over the past few decades, there has been a correlation between many chronic diseases and lifestyle choices. In this present era of advanced communication, technical devices, and modern technology, the level of convenience at our disposal has increased. However, it has also led to an increase in problems with mental and emotional health. Lifestyle diseases or Non-Communicable Diseases include conditions i.e including lung cancer, diabetes, asthma, Coronary Heart Disease (CHD), and other illnesses. Interestingly, the same modern technologies like Artificial Intelligence, Computational Intelligence (CI) Internet of Things, Internet of Medical Things, Explainable AI, 5G enabled Telemedicine etc. are being used to treat, manage and prevent such life style disorders in an efficient manner. One among such technologies is CI. CI refers to ability of a computer to learn specific task from data or experimental observation.
In recent years, CI technologies received attention from a wide range of fields, including the health field. The advancement of CI in medicine is driven by the growth of software applications and computer hardware, as well as the digitalization of data pertaining to health. The future of CI in healthcare is being shaped by these developments, which also present exciting possibilities as well as challenges. Exploring the various dimensions of application of CI in Healthcare and discussing its potential, drawbacks, are the main objectives of this Research Topic.
The purpose of this Research Topic is to present latest advancements in research on the use of CI methods to treat lifestyle diseases. This collection attempts to investigate how these approaches can improve initiatives pertaining to public health and enable people to make decisions that lead to healthier lifestyles.
The specific goals of this Research Topic include:
a. Exploring innovative approaches in the management of Non-Communicable Diseases;
b. Developing CI-based predictive models to assess and forecast the risk of Non-Communicable Diseases early;
c. Utilizing CI to examine large datasets, discover population-level trends, and enhance public health interventions;
d. Encouraging the submission of papers that demonstrate the real-world impact of CI applications;
This Research Topic invites contributions covering, but not limited to, the following topics:
1. Innovative applications of CI in the prediction and prevention of lifestyle-related Non-Communicable Diseases (NCDs);
2. Development of personalized health interventions using CI tools;
3. CI integration with wearable health technologies for continuous lifestyle monitoring;
4. The role of mobile health applications leveraging CI for public health improvement;
5. Ethical considerations and data privacy challenges in CI-driven public health interventions;
6. CI-based decision support systems for the management of lifestyle-related disorders;
7. Real-world impact of CI applications in public health interventions and disease management;
8. CI-driven predictive models for multi-factorial lifestyle-related diseases;
9. Role of CI in creating adaptive interventions for chronic lifestyle diseases;
10. Using CI to analyze population-level data for public health strategies-
In recent years, CI technologies received attention from a wide range of fields, including the health field. The advancement of CI in medicine is driven by the growth of software applications and computer hardware, as well as the digitalization of data pertaining to health. The future of CI in healthcare is being shaped by these developments, which also present exciting possibilities as well as challenges. Exploring the various dimensions of application of CI in Healthcare and discussing its potential, drawbacks, are the main objectives of this Research Topic.
The purpose of this Research Topic is to present latest advancements in research on the use of CI methods to treat lifestyle diseases. This collection attempts to investigate how these approaches can improve initiatives pertaining to public health and enable people to make decisions that lead to healthier lifestyles.
The specific goals of this Research Topic include:
a. Exploring innovative approaches in the management of Non-Communicable Diseases;
b. Developing CI-based predictive models to assess and forecast the risk of Non-Communicable Diseases early;
c. Utilizing CI to examine large datasets, discover population-level trends, and enhance public health interventions;
d. Encouraging the submission of papers that demonstrate the real-world impact of CI applications;
This Research Topic invites contributions covering, but not limited to, the following topics:
1. Innovative applications of CI in the prediction and prevention of lifestyle-related Non-Communicable Diseases (NCDs);
2. Development of personalized health interventions using CI tools;
3. CI integration with wearable health technologies for continuous lifestyle monitoring;
4. The role of mobile health applications leveraging CI for public health improvement;
5. Ethical considerations and data privacy challenges in CI-driven public health interventions;
6. CI-based decision support systems for the management of lifestyle-related disorders;
7. Real-world impact of CI applications in public health interventions and disease management;
8. CI-driven predictive models for multi-factorial lifestyle-related diseases;
9. Role of CI in creating adaptive interventions for chronic lifestyle diseases;
10. Using CI to analyze population-level data for public health strategies-
Keywords: Smart Health Management, Intelligent Diabetes Management, Healthcare Analytics, AI in Healthcare, Smart Chronic Disease Monitoring, Computational Intelligence driven Health Intervention, Heart Disease Prevention, AI based Lifestyle Modification
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
