About this Research Topic
With the surge in patient numbers, specialists have sought a balance between patients and caretakers, paving the way for valued healthcare and patient data. Hybrid Health Care Units combine technology and human care, offering an innovative approach that merges non-profit and for-profit aspects. This model has become the preferred solution for delivering innovative, cost-effective health care. By consolidating healthcare services in one location, these organizations improve resource sharing, enhancing care and reducing costs.
The Cluster-based Intelligent Recommendation System analyzes user data via a complex network, obtaining a preference shift-vector based on user preferences. It uses a scale-free stochastic block model to identify clusters of similar users. Hybrid healthcare units provide supportive, compassionate care, and leverage enhanced technologies to deliver high-quality, safe, and sustainable healthcare services. Intelligent information systems improve staff performance and ease pressure on health service systems.
The hybrid intelligent system, Cluster-based Intelligent Recommendation System in Hybrid Healthcare Units, has successfully been applied in cluster-based recommendation systems. This system employs a hierarchical clustering algorithm and artificial neural network classifier. It applies to various aspects of patients' information, including conventional and complementary medicine. It serves as an optimal tool to alleviate suffering, improve quality of life, and support individuals throughout all stages of illness. Users are initially grouped using the k-means clustering approach, and treatment recommendations are obtained based on disease or symptoms using association rule mining techniques.
A hybrid system combines disease and symptoms-based recommendations with collaborative filtering to generate the final set of treatments. Personalization is a major challenge in healthcare information technology, but intelligent systems like the Cluster-based Intelligent Recommendation System enable personalized recommendations in hybrid healthcare units. An intelligent healthcare management system handles the cluster problem, employing clustering algorithms and distance metrics for prediction, analysis, and decision-making based on patients' medical history. While challenges such as performance and scalability exist, insights from practitioners and case studies are welcomed. However, the server approach may compromise privacy due to data synchronization.
The goal of this Research Topic is to explore the innovative business approach offered by Hybrid Health Care Units, which combine non-profit and for-profit entities to achieve cost-effective and successful healthcare. The Cluster-based Intelligent Recommendation System plays a crucial role in this context by analyzing user data and identifying clusters of similar users. In hybrid healthcare units, this system is utilized alongside conventional and complementary medicine to alleviate suffering and enhance quality of life.
We strongly encourage researchers to submit articles that focus on, but are not limited to, the following fields:
1. Semi-supervised learning techniques and topics discovery methods to obtain a high-level representation of items
2. Clustering techniques to group all users into several disjoint clusters
3. Cluster-based recommendation system for patients suffering from Complex diseases
4. Intelligent Recommendation System in hybrid health care units
5. Clustering prepared the dataset for hybrid health care
6. Efficient method for multicast routing schemes for Hybrid Healthcare Units
7. Unified Automatic Intelligent Recommendation System in health care
8. Incremental Clustering by Categorical Optimization in health care
9. Study on k-means clustering and Tensor Decomposition Network
10. Fuzzy Preference Logic theory for hybrid health care
11. Hybrid IR System to exploit the benefits of both content-based and collaborative approaches
12. Cluster-based Intelligent recommendation System using topic modelling and clustering techniques
This Research Topic welcomes all article types available in Frontiers in Medicine including Original Research, Reviews and Mini Reviews, Method and Perspective Articles as well as Hypothesis and Theory Articles
The Cluster-based Intelligent Recommendation System analyzes user data via a complex network, obtaining a preference shift-vector based on user preferences. It uses a scale-free stochastic block model to identify clusters of similar users. Hybrid healthcare units provide supportive, compassionate care, and leverage enhanced technologies to deliver high-quality, safe, and sustainable healthcare services. Intelligent information systems improve staff performance and ease pressure on health service systems.
The hybrid intelligent system, Cluster-based Intelligent Recommendation System in Hybrid Healthcare Units, has successfully been applied in cluster-based recommendation systems. This system employs a hierarchical clustering algorithm and artificial neural network classifier. It applies to various aspects of patients' information, including conventional and complementary medicine. It serves as an optimal tool to alleviate suffering, improve quality of life, and support individuals throughout all stages of illness. Users are initially grouped using the k-means clustering approach, and treatment recommendations are obtained based on disease or symptoms using association rule mining techniques.
A hybrid system combines disease and symptoms-based recommendations with collaborative filtering to generate the final set of treatments. Personalization is a major challenge in healthcare information technology, but intelligent systems like the Cluster-based Intelligent Recommendation System enable personalized recommendations in hybrid healthcare units. An intelligent healthcare management system handles the cluster problem, employing clustering algorithms and distance metrics for prediction, analysis, and decision-making based on patients' medical history. While challenges such as performance and scalability exist, insights from practitioners and case studies are welcomed. However, the server approach may compromise privacy due to data synchronization.
The goal of this Research Topic is to explore the innovative business approach offered by Hybrid Health Care Units, which combine non-profit and for-profit entities to achieve cost-effective and successful healthcare. The Cluster-based Intelligent Recommendation System plays a crucial role in this context by analyzing user data and identifying clusters of similar users. In hybrid healthcare units, this system is utilized alongside conventional and complementary medicine to alleviate suffering and enhance quality of life.
We strongly encourage researchers to submit articles that focus on, but are not limited to, the following fields:
1. Semi-supervised learning techniques and topics discovery methods to obtain a high-level representation of items
2. Clustering techniques to group all users into several disjoint clusters
3. Cluster-based recommendation system for patients suffering from Complex diseases
4. Intelligent Recommendation System in hybrid health care units
5. Clustering prepared the dataset for hybrid health care
6. Efficient method for multicast routing schemes for Hybrid Healthcare Units
7. Unified Automatic Intelligent Recommendation System in health care
8. Incremental Clustering by Categorical Optimization in health care
9. Study on k-means clustering and Tensor Decomposition Network
10. Fuzzy Preference Logic theory for hybrid health care
11. Hybrid IR System to exploit the benefits of both content-based and collaborative approaches
12. Cluster-based Intelligent recommendation System using topic modelling and clustering techniques
This Research Topic welcomes all article types available in Frontiers in Medicine including Original Research, Reviews and Mini Reviews, Method and Perspective Articles as well as Hypothesis and Theory Articles
Keywords: Scale-free Stochastic block model, Graph-based recommender system, Artificial neural network classifier, Decision-making, Hierarchical clustering algorithm, Hybrid intelligent system, Collaborative filtering method, Personalization
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.
