Healthcare Recommender Research Articles

An Integrative Framework for Healthcare Recommendation Systems: Leveraging the Linear Discriminant Wolf–Convolutional Neural Network (LDW-CNN) Model

In the evolving healthcare landscape, recommender systems have gained significant importance due to their role in predicting and anticipating a wide range of health-related data for both patients and healthcare professionals. These systems are crucial for delivering precise information while adhering to high standards of quality, reliability, and authentication. Objectives: The primary objective of this research is to address the challenge of class imbalance in healthcare recommendation systems. This is achieved by improving the prediction and diagnostic capabilities of these systems through a novel approach that integrates linear discriminant wolf (LDW) with convolutional neural networks (CNNs), forming the LDW-CNN model. Methods: The LDW-CNN model incorporates the grey wolf optimizer with linear discriminant analysis to enhance prediction accuracy. The model’s performance is evaluated using multi-disease datasets, covering heart, liver, and kidney diseases. Established error metrics are used to compare the effectiveness of the LDW-CNN model against conventional methods, such as CNNs and multi-level support vector machines (MSVMs). Results: The proposed LDW-CNN system demonstrates remarkable accuracy, achieving a rate of 98.1%, which surpasses existing deep learning approaches. In addition, the model improves specificity to 99.18% and sensitivity to 99.008%, outperforming traditional CNN and MSVM techniques in terms of predictive performance. Conclusions: The LDW-CNN model emerges as a robust solution for multidisciplinary disease prediction and recommendation, offering superior performance in healthcare recommender systems. Its high accuracy, alongside its improved specificity and sensitivity, positions it as a valuable tool for enhancing prediction and diagnosis across multiple disease domains.

Recommender System for E-Health

Introduction; E-healthcare management services can be significantly enhanced through the implementation of recommender systems, as highlighted in various research papers. These systems, such as Healthcare Recommender Systems (HRS) and Health Care Recommender Systems (HCRS), utilize advanced algorithms and machine learning techniques to provide personalized health recommendations based on user input and medical data. Objective; Recommend healthcare services based on patient's state. Model healthcare information network for efficient service recommendation.Methodology; Recommends healthcare services based on patient's critical situation and requirements. Offers re-configurable healthcare workflows to medical staff. Machine learning method classification is applied using decision tree and its result is presented which reflects 70 to 75% accuracy in predictive models which ensure that health recommender system is a full proof system.Result; Hospital recommender systems represent a significant advancement in healthcare, providing personalized and data-driven recommendations to patients.Conclusion; The integration of recommender systems in e-healthcare management services holds great potential in improving personalized patient care, promoting health awareness, and optimizing the quality of healthcare recommendations. In this paper author analyzed and estimated the level of accuracy of recommendation systems in healthcare for personalized medical treatment. It surveys current applications, challenges, and future directions in this field.

A Healthcare Recommender System Framework

After the pandemic hit every part of the world, healthcare awareness is slowly rising among every human being, especially leaders of each country. Due to a shortage of manpower in the healthcare industry, patients tend to search the internet for some self diagnoses. This way is extremely dangerous as patients might end up using the wrong treatment such as taking the wrong medication to treat their sickness since there are so many different remedies posted on the internet without valid recognition from the healthcare professionals. To aid in overcoming this problem, this research will be building a Healthcare Recommender System. The goal of a Healthcare Recommender System (HRS) aims to supply its user with medical information that is meant to be highly relevant and tailored to an individuals need. Hence, this paper gives an overview of various recommender systems, datasets employed, and evaluation metrics used in the healthcare system. In addition, we propose the framework for the HRS to capture user input on their condition and recommend the next course of action. The steps involved in our recommender system includes choosing the dataset and techniques, data cleaning and preprocessing, building the recommender system, training the recommender engine, and finally performing the prediction. We generate the accuracy of prediction and analyze with some results. From the experimental results, Cosine Similarity has the highest accuracy compared to Jaccard Similarity and Euclidean Distance.

A Healthcare Recommender System Framework

After the pandemic hit every part of the world, healthcare awareness is slowly rising among every human being, especially leaders of each country. Due to a shortage of manpower in the healthcare industry, patients tend to search the internet for some self diagnoses. This way is extremely dangerous as patients might end up using the wrong treatment such as taking the wrong medication to treat their sickness since there are so many different remedies posted on the internet without valid recognition from the healthcare professionals. To aid in overcoming this problem, this research will be building a Healthcare Recommender System. The goal of a Healthcare Recommender System (HRS) aims to supply its user with medical information that is meant to be highly relevant and tailored to an individuals need. Hence, this paper gives an overview of various recommender systems, datasets employed, and evaluation metrics used in the healthcare system. In addition, we propose the framework for the HRS to capture user input on their condition and recommend the next course of action. The steps involved in our recommender system includes choosing the dataset and techniques, data cleaning and preprocessing, building the recommender system, training the recommender engine, and finally performing the prediction. We generate the accuracy of prediction and analyze with some results. From the experimental results, Cosine Similarity has the highest accuracy compared to Jaccard Similarity and Euclidean Distance.

A novel approach to enhance the quality of health care recommender system using fuzzy-genetic approach

In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user’s or patient’s illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user’s extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine.

Effective healthcare service recommendation with network representation learning: A recursive neural network approach

Recently, recommender systems have been combined with healthcare systems to recommend needed healthcare items for both patients and medical staff. By monitoring the patients’ states, healthcare services and their consumed smart medical objects can be recommended to a medical team according to the patient’s critical situation and requirements. However, a common drawback of the few existing solutions lies in the limited modeling of the healthcare information network. In addition, current solutions do not consider the typed nature of healthcare items. Moreover, existing healthcare recommender systems lack flexibility, and none of them offers re-configurable healthcare workflows to medical staff. In this paper, we take advantage of collaborative filtering and representation learning principles, by proposing a method for the recommendation of healthcare services. These latter follow a predefined execution pattern, i.e. treatment/medication workflow, that is determined by our framework depending on the patient’s state. To achieve this goal, we model the healthcare information network as a knowledge graph. This latter, based on an incremental learning method, is then transformed into a cuboid space to facilitate its processing. That is by learning latent representations of its content (e.g., smart objects, healthcare services, patients symptoms, etc.). Finally, a collaborative recommendation method is defined to select the high-quality healthcare services that will be composed and executed according to a determined workflow model. Experimental results have proven the efficiency of our solution in terms of recommended services’ quality.

Healthcare Recommender System Based on Medical Specialties, Patient Profiles, and Geospatial Information

The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and communication technologies to bring sustainability to health services. This paper proposes a novel, methodological, and collaborative approach based on patient-centered technology, which consists of a recommender system architecture to assist the health service level according to medical specialties. The system provides recommendations according to the user profile of the citizens and a ranked list of medical facilities. Thus, we propose a health attention factor to semantically compute the similarity between medical specialties and offer medical centers with response capacity, health service type, and close user geographic location. Thus, considering the challenges described in the state-of-the-art, this approach tackles issues related to recommenders in mobile devices and the diversity of items in the healthcare domain, incorporating semantic and geospatial processing. The recommender system was tested in diverse districts of Mexico City, and the spatial visualization of the medical facilities filtering by the recommendations is displayed in a Web-GIS application.

Recommender systems in the healthcare domain: state-of-the-art and research issues

Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.

Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

SummaryThe evolution of intelligent and data‐driven systems has pushed for the tectonic transition from ancient medication to human‐centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient‐centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state‐of‐the‐art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human‐centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future.

Developing a Healthcare Recommender System Using an Enhanced Symptoms-Based Collaborative Filtering Technique

Developing a Healthcare Recommender System Using an Enhanced Symptoms-Based Collaborative Filtering Technique

Collaborative Variational Deep Learning for Healthcare Recommendation

Healthcare recommender system (HRS) has shown the great potential of targeting medical experts or patients, and plays a key role in improving an individual’s health by providing insightful recommendations. The HRSs generate recommendations based on a successful and widely applied method known as collaborative filtering (CF). Despite its success, the CF suffers from data sparsity and cold-start problem, which results in the poor quality of recommendations. In particular, it is a great challenge to seeking information relevant to patients’ condition, and understanding the medical terms and relationships between them in HRSs. To address these problems, we design a novel collaborative variational deep learning model (CVDL) to exploit multi-sourced information for providing appropriate healthcare recommendations in primary care service. CVDL employs additional variational autoencoder (VAE) to learn deep latent representations for item contents (the description of primary care doctors) in latent space, instead of observation space through an inference network. Meanwhile, the CVDL extracts latent user (patient) features by incorporating user profile in a VAE neural network. Therefore, the CVDL can learn better implicit relationships between items and users from item content, user profile, and rating matrix. In addition, a Stochastic Gradient Variational Bayes (SGVB) approach is proposed to calculate the maximum posterior estimates for learning model parameters. The experiments conducted on three datasets have indicated that our method significantly outperforms the state-of-the-art hybrid CF methods.

Computing Hierarchical Summary from Two-Dimensional Big Data Streams

There are many application domains, where hierarchical data is inherent, but surprisingly, there are few techniques for mining patterns from such important data. Hierarchical Heavy Hitters (HHH) and multilevel and Cross-Level Association Rules (CLAR) mining are well-known hierarchical pattern mining techniques. The problem in these techniques; however, is that they focus on capturing only global patterns from data but cannot identify local contextual patterns. Another problem in these techniques is that they treat all data items in the transaction equally and do not consider the sequential nature of the relationship among items within a transaction; hence, they cannot capture the correlation semantic within the transactions of the data items. There are many applications such as clickstream mining, healthcare data mining, network monitoring, and recommender systems, which require to identify local contextual patterns and correlation semantics. In this work, we introduce a new concept, which can capture the sequential nature of the relationship between pairs of hierarchical items at multiple concept levels and can capture local contextual patterns within the context of the global patterns. We call this notion Hierarchically Correlated Heavy Hitters (HCHH). Specifically, the proposed approach finds the correlation between items corresponding to hierarchically discounted frequency counts. We have provided formal definitions of the proposed concept and developed algorithmic approaches for computing HCHH in data streams efficiently. The proposed HCHH algorithm have deterministic error guarantees, and space bounds. It requires $O(\frac{\eta}{\epsilon _p\epsilon _s})$ memory, where $\eta$ is a small constant, and $\epsilon _p\in$ [0,1], $\epsilon _s\in$ [0,1] are user defined parameters on upper bounds of estimation error. We have compared the proposed HCHH concept with existing hierarchical pattern mining approaches both theoretically as well as experimentally.

An efficient multi-party scheme for privacy preserving collaborative filtering for healthcare recommender system

Patient oriented decision-making in medical domains can enhance the efficiency of the modern healthcare recommender system provided the data scattered across different geographical regions is collected, mined and analyzed efficiently. Different sites, having Arbitrary Distributed Data (ADD) of healthcare services at various nodes can collaborate with each other to generate customer’s preference leading to mutual advantage and overcoming of the issues related to insufficient ratings of various medical services. However, due to privacy, financial and legal issues, different parties defer from sharing their confidential data. If the parties are assured of data confidentiality, they might agree for fruitful collaboration. Few existing studies proposed Privacy Preserving Collaborative Filtering (PPCF) on ADD, but these techniques considered only two parties. Moreover, the computation cost of off-line model generation process is high since these techniques use homomorphic encryption techniques. To fill these gaps, this paper propose PPCF scheme on ADD based on multi-party random masking and polynomial aggregation techniques. In the proposal, two phases are considered namely as: off-line model generation and online prediction generation. Three protocols have been considered for privacy preservation so that analysis of each protocol is performed separately. The Paillier homomorphic encryption system is also used to calculate the length of vectorX securely, so that only additive property of homomorphic encryption is used. Analysis of the proposed scheme has been done for security, accuracy, coverage and performance on healthcare and Movieslens datasets. It has been experimentally demonstrated that the proposed scheme maintains data owner’s confidentiality, and privacy measure so that it does not affect the accuracy of prediction generation on integrated data. Comparative analysis of the proposed scheme has also been done with other related schemes based on off-line and online computation overheads. The results obtained demonstrated that the proposed scheme has significant improvement by a factor of 36% (approx) with respect to the aforementioned parameters.

A new computing environment for collective privacy protection from constrained healthcare devices to IoT cloud services

The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.

Multi-view non-negative tensor factorization as relation learning in healthcare data.

Discovering patterns in co-occurrences data between objects and groups of concepts is a useful task in many domains, such as healthcare data analysis, information retrieval, and recommender systems. These relational representations come from objects' behaviors in different views, posing a challenging task of integrating information from these views to uncover the shared latent structures. The problem is further complicated by the high dimension of data and the large ratio of missing data. We propose a new paradigm of learning semantic relations using tensor factorization, by jointly factorizing multi-view tensors and searching for a consistent underlying semantic space across each views. We formulate the idea as an optimization problem and propose efficient optimization algorithms, with a special treatment of missing data as well as high-dimensional data. Experiments results show the potential and effectiveness of our algorithms.

A Fog Based Middleware for Automated Compliance With OECD Privacy Principles in Internet of Healthcare Things

Cloud-based healthcare service with the Internet of Healthcare Things (IoHT) is a model for healthcare delivery for urban areas and vulnerable population that utilizes the digital communications and the IoHT to provide flexible opportunities to transform all the health data into workable, personalized health insights, and help attain wellness outside the traditional hospital setting. This model of healthcare Web services acts like a living organism, taking advantage of the opportunities afforded by running in cloud infrastructure to connect patients and providers anywhere and anytime to improve the quality of care, with the IoHT, acting as a central nervous system for this model that measures patients' vital statistics, constantly logging their health data, and report any abnormalities to the relevant healthcare provider. However, it is crucial to preserve the privacy of patients while utilizing this model so as to maintain their satisfaction and trust in the offered services. With the increasing number of cases for privacy breaches of healthcare data, different countries and corporations have issued privacy laws and regulations to define the best practices for the protection of personal health information. The health insurance portability and accountability act and the privacy principles established by the Organization for Economic Cooperation and Development (OECD) are examples of such regulation frameworks. In this paper, we assert that utilizing the cloud-based healthcare services to generate accurate health insights are feasible, while preserving the privacy of the end-users' sensitive health information, which will be residing on a clear form only on his/her own personal gateway. To support this claim, the personal gateways at the end-users' side will act as intermediate nodes (called fog nodes) between the IoHT devices and the cloud-based healthcare services. In such solution, these fog nodes will host a holistic privacy middleware that executes a two-stage concealment process within a distributed data collection protocol that utilizes the hierarchical nature of the IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. Additionally, the proposed solution complies with one of the common privacy regulation frameworks for fair information practice in a natural and functional way-which is OECD privacy principles. We depicted how the proposed approach can be integrated into a scenario related to preserving the privacy of the users' health data that is utilized by a cloud-based healthcare recommender service in order to generate accurate referrals. Our holistic approach induces a straightforward solution with accurate results, which are beneficial to both end-users and service providers.

Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender Systems

This systematic literature review is aimed at examining empirical results and practical implementations of healthcare recommender systems. While fundamentally many of the development of recommender systems in medical and healthcare are based on theory and logic, the performance is always measured in terms of empirical results and practical implementations from evaluation of such systems. Besides, the ultimate judgment of the effectiveness of the methods and algorithms used is often based on the empirical results of recommender systems. Robustness, efficiency, speed, and accuracy are also best determined by empirical results. Extensive search was carried out in some major databases. Literature were grouped into three categories namely core, related, and relevant. The core papers were subjected to further analysis. The result shows that most work reviewed were partially evaluated and have a promising future. Moreover, a yet-to-be explored novel proposal for integration of a recommender system into smart home care is presented.