Doctor Recommendation Based on an Intuitionistic Normal Cloud Model Considering Patient Preferences

Online Medical Consultation Service-Oriented Recommendations: Systematic Review

BACKGROUND

Online health communities have given rise to a new e-service known as online medical consultations (OMCs), enabling remote interactions between physicians and patients. To address challenges, such as patient information overload and uneven distribution of physician visits, online health communities should develop OMC-oriented recommenders.

OBJECTIVE

We aimed to comprehensively investigate what paradigms lead to the success of OMC-oriented recommendations.

METHODS

A literature search was conducted through e-databases, including PubMed, ACM Digital Library, Springer, and ScienceDirect, from January 2011 to December 2023. This review included all papers directly and indirectly related to the topic of health care-related recommendations for online services.

RESULTS

The search identified 611 articles, of which 26 (4.3%) met the inclusion criteria. Despite the growing academic interest in OMC recommendations, there remains a lack of consensus among researchers on the definition of e-service-oriented recommenders. The discussion highlighted 3 key factors influencing recommender success: features, algorithms, and metrics. It advocated for moving beyond traditional e-commerce-oriented recommenders to establish an innovative theoretical framework for e-service-oriented recommenders and addresses critical technical issues regarding 2-sided personalized recommendations.

CONCLUSIONS

This review underscores the essence of e-services, particularly in knowledge- and labor-intensive domains such as OMCs, where patients seek interpretable recommendations due to their lack of domain knowledge and physicians must balance their energy levels to avoid overworking. Our study's findings shed light on the importance of customizing e-service-oriented personalized recommendations to meet the distinct expectations of 2-sided users considering their cognitive abilities, decision-making perspectives, and preferences. To achieve this, a paradigm shift is essential to develop unique attributes and explore distinct content tailored for both parties involved.

An intelligent medical guidance and recommendation model driven by patient-physician communication data

Based on the online patient-physician communication data, this study used natural language processing and machine learning algorithm to construct a medical intelligent guidance and recommendation model. First, based on 16,935 patient main complaint data of nine diseases, this study used the word2vec, long-term and short-term memory neural networks, and other machine learning algorithms to construct intelligent department guidance and recommendation model. Besides, taking ophthalmology as an example, it also used the word2vec, TF-IDF, and cosine similarity algorithm to construct an intelligent physician recommendation model. Furthermore, to recommend physicians with better service quality, this study introduced the information amount of physicians' feedback to the recommendation evaluation indicator as the text and voice service quality. The results show that the department guidance model constructed by long-term and short-term memory neural networks has the best effect. The precision is 82.84%, and the F1-score is 82.61% in the test set. The prediction effect of the LSTM model is better than TextCNN, random forest, K-nearest neighbor, and support vector machine algorithms. In the intelligent physician recommendation model, under certain parameter settings, the recommendation effect of the hybrid recommendation model based on similar patients and similar physicians has certain advantages over the model of similar patients and similar physicians.

Using machine learning to explore the determinants of service satisfaction with online healthcare platforms during the COVID-19 pandemic

This study investigates the determinants of service satisfaction with online healthcare platforms using machine learning (ML) algorithms. By training and testing eleven ML models based on data mined from a leading online healthcare platform in China, we obtained the best-performing ML algorithm for service satisfaction prediction, namely, Light Gradient Boosting Machine. Furthermore, our empirical results indicate that gifts, patient votes, popularity, fee-based consultation volume, gender, and thank-you letters positively impact service satisfaction, while the impacts of consultation volume, free consultation volume, views, waiting time, articles, physician title, and hospital level are negative. We discuss the theoretical and managerial implications.

Predicting Patients' Satisfaction With Doctors in Online Medical Communities

Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients’ satisfaction with doctors and predicting their satisfaction can help patients narrow down their choices and increase their loyalty towards online medical communities. Considering the imbalanced feature of dataset collected from Good Doctor, we integrated XGBoost and SMOTE algorithm to examine what factors and these factors can be used to predict patient satisfaction. SMOTE algorithm addresses the imbalanced issue by oversampling imbalanced classification datasets. And XGBoost algorithm is an ensemble of decision trees algorithm where new trees fix errors of existing trees. The experimental results demonstrate that SMOTE and XGBoost algorithm can achieve better performance. We further analyzed the role of features played in satisfaction prediction from two levels: individual feature level and feature combination level.

Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients' ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients' EMRs and specialists' long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists' activity and patients' perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists' profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists' activity and patients' perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients' demand and service providers' capabilities, and providing beneficial insights for data-driven telemedicine services.

Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic

Among the set of parameters for which data are collected for decision-making based on artificial intelligence methods, often only some of the parameters are significant. This article compares methods for determining the significant parameters based on the theory of mathematical statistics, and fuzzy and boolean logic. The testing was conducted on several test data sets with a different number of parameters and different variability of parameter values. It was shown that for data sets with a small number of parameters (<5), the most accurate result was given for a method based on the theory of mathematical statistics and boolean logic. For a data set with a large number of parameters—the most suitable is the method of fuzzy logic.

A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.

Managing consistency and consensus measures and adjustment strategies in group decision making with probabilistic linguistic preference relations

The use of probabilistic linguistic preference relations (PLPRs) in pairwise comparisons enhances the flexibility of quantitative decision making. To promote the application of probabilistic linguistic term sets (PLTSs) and PLPRs, this paper introduces the consistency and consensus measures and adjustment strategies to guarantee the rationality of preference information utilized in the group decision making process. First of all, a novel entropy-based similarity measure is developed with PLTSs. Hereafter an improved consistency measure is defined on the basis of the proposed similarity measure, and a convergent algorithm is constructed to deal with the consistency improving process. Furthermore, a similarity-based consensus measure is developed in a given PLPR, and the consensus reaching process is presented to deal with the unacceptable consensus degree. The proposed consistency improving and consensus reaching processes follow a principle of minimum information loss, called a local adjustment strategy. In particular, the presented methods not only overcome the deficiencies in existing studies but also enhance the interpretation and reduce the complexity of the group decision making process. Finally, the proposed consistency measure and improving process, as well as consensus measure and reaching process are verified through a numerical example for the medical plan selection issue. The result and in-depth comparison analysis validate the feasibility and effectiveness of the proposed methods.

A Hybrid Fuzzy Profiling-Nonnegative Latent Factor Model Considering Consumer Preference at Different Levels

The implicit feedback information associated with actual behavior can symmetrically reflect consumer preference, which is valuable and deserves a deep investigation. Considering the inherent uncertainty and fuzziness of implicit data, the question of how to depict consumer preference in such data is always a critical and difficult issue. In this paper, a fuzzy profiling-nonnegative latent factor (FP-NLF) model is proposed to address this problem. First, a fuzzy profiling (FP) procedure is designed for characterization of consumer preference at various levels, where the fuzzy set is introduced to manage the uncertainty and fuzziness of implicit data. Two series of strategies are provided to determine the comprehensive preference for different scenarios and commercial intentions. Subsequently, a nonnegative latent factor (NLF) model is adopted to make predictions in the case of notably sparse data. Finally, a higher quality recommendation is ultimately produced for which only the products satisfying given preference level are recommended. In addition, a case study with real data is conducted for a detailed demonstration, and the results reveal the feasibility and superiority of our proposal through comparative analysis. Finally, the sensitivity analysis explores the influences of changing weights of strategy, which can provide guidance for developing purposeful strategies to better serve consumers.

An Interval Type-2 Fuzzy Similarity-Based MABAC Approach for Patient-Centered Care

Patient-centered care is an essential part of the implementation of integrated medicine, integrating humanistic care into nursing services, enhancing communication between caregivers and patients, and providing personalized service to patients. Based on the similarity of interval type-2 fuzzy numbers (IT2FNs), a novel similarity-based methodology is presented for the selection of the most suitable medical treatment under a patient-centered environment. First, we propose a new similarity based on the geometric properties of interval type-2 fuzzy numbers and present a new property based on the center of gravity. Meanwhile, in order to better highlight the advantages of the proposed similarity, we selected 30 samples for comparative experiments. Second, considering the straightforward logic of the multi-attributive border approximation area comparison (MABAC) method, we extended it based on similarity to make the decision more accurate. Finally, a realistic patient-centered type-2 diabetes treatment selection problem is presented to verify the practicality and effectiveness of the proposed algorithm. A comparative analysis with existing methods is also described.

A Novel Interval Three-Way Concept Lattice Model with Its Application in Medical Diagnosis

Medical diagnosis has been recognized as one of the key processes in clinical medicine, which determines diseases from some given symptoms. Nonetheless, previous works about medical diagnosis have some drawbacks because medical data are usually fuzzy, uncertain, incomplete and imprecise. To achieve the optimal medical diagnosis decision by reducing cost and enhancing accuracy, this paper develops a new method named interval three-way concept lattice model. Firstly, we redefine the decision rules and metric function of three-way decision based on interval concept lattice. Secondly, we build a visualized hierarchical structure of relationship between concepts through interval concept construction algorithm which helps us to make decision preferably and clearly. Finally, we establish a dynamic strategy optimization model for medical diagnosis decision making. In addition, a medical case demonstrates the effectiveness and feasibility of this proposed model.