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.

To predict air quality (PM2.5 concentrations, et al), many parametric regression models have been developed, while deep learning algorithms are used less often. And few of them takes the air pollution emission or spatial information into consideration or predict them in hour scale. In this paper, we proposed a spatialtemporal GRU-based prediction framework incorporating ground pollution monitoring (GPM), factory emissions (FE), surface meteorology monitoring (SMM) variables to predict hourly PM2.5 concentrations. The dataset for empirical experiments was built based on air quality monitoring in Shenyang, China. Experimental results indicate that our method enables more accurate predictions than all baseline models and by applying the convolutional processing to the GPM and FE variables notable improvement can be achieved in prediction accuracy.

Particulate matter PM_2.5 pollution affects the Chinese population, particularly in cities such as Shenyang in northeastern China, which occupies a number of traditional heavy industries. This paper proposes a semi-supervised learning model used for predicting PM_2.5 concentrations. The model incorporates rich data from the real world, including 11 air quality monitoring stations in Shenyang and nearby cities. There are three types of data: air monitoring, meteorological data, and spatiotemporal information (such as the spatiotemporal effects of PM_2.5 emissions and diffusion across different geographical regions). The model consists of two classifiers: genetic programming (GP) to forecast PM_2.5 concentrations and support vector classification (SVC) to predict trends. The experimental results show that the proposed model performs better than baseline models in accuracy, including 3% to 18% over a classic multivariate linear regression (MLR), 1% to 11% over a multi-layer perceptron neural network (MLP-ANN), and 21% to 68% over a support vector regression (SVR). Furthermore, the proposed GP approach provides an intuitive contribution analysis of factors for PM_2.5 concentrations. The data of backtracking points adjacent to other monitoring stations are critical in forecasting shorter time intervals (1 h). Wind speeds are more important in longer intervals (6 and 24 h).

Online Medical Consultations (OMCs) are increasingly prevalent in Online Healthcare Communities (OHCs), with a considerable focus on doctor recommendations. However, existing research has predominantly centered on recommending individual specialized doctors, neglecting the exploration of more reliable interdisciplinary options for collaborative consultations. This paper introduces an innovative framework for interdisciplinary doctor recommendations for collaborative work in OMC scenarios, comprising two modules: one for calculating expertise and another for collaborative computing. Leveraging the medical knowledge graph, the former employs an expertise encoder to derive specialty embeddings for patient queries, doctor profiles, and historical consultations. The latter builds a collaborative circle for each doctor based on three dimensions: scientific citation, academic collaboration, and spatiotemporal proximity. This circle analysis delves into the potential for cooperation between doctors, aiming to identify optimal combinations. Experimental results on two real datasets demonstrate the superior performance of our proposed model compared to state-of-the-art methods. Ablation studies highlight the significant contribution of the three social network dimensions and their interactions to collaborative computing outcomes. Our model exhibits robustness across different parameter settings, as demonstrated through experiments. The study's results empower practitioners to develop a portfolio of recommendations, enhancing the effectiveness of cooperative consultations for complex diseases requiring multidisciplinary collaboration.