Entropy-Enhanced Attention Model for Explanation Recommendation.

Most of the existing recommendation systems using deep learning are based on the method of RNN (Recurrent Neural Network). However, due to some inherent defects of RNN, recommendation systems based on RNN are not only very time consuming but also unable to capture the long-range dependencies between user comments. Through the sentiment analysis of user comments, we can better capture the characteristics of user interest.

Optimal Product Substitution and Dual Sourcing Strategy considering Reliability of Production Lines.

Most of the supply chain literature assumes that product substitution is an effective method to mitigate supply chain disruptions and that all production lines either survive or are disrupted together. Such assumptions, however, may not hold in the real world: (1) when there is a shortfall of all products, product substitution may be inadequate unless it is paired with other strategies such as dual sourcing; and (2) production lines do not survive forever and may fail. To relax such assumptions, this paper therefore investigates the situations that the manufacturer may optimize substitution policy and dual sourcing policy to cope with supply chain disruptions.

The Effects of Log-in Behaviors and Web Reviews on Patient Consultation in Online Health Communities: Longitudinal Study.

Background: With the rapid development of information technology and web-based communities, a growing number of patients choose to consult physicians in online health communities (OHCs) for information and treatment. Although extant research has primarily discussed factors that influence the consulting choices of OHC patients, there is still a lack of research on the effects of log-in behaviors and web reviews on patient consultation.

Objective: This study aims to explore the impact of physicians' log-in behavior and web reviews on patient consultation.

Social Network Analysis of an Online Smoking Cessation Community to Identify Users' Smoking Status.

Objectives: Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in identifying the smoking status of OSCC users ("quit" vs. "not quit").

Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach.

Introduction: An increasing number of patients are voicing their opinions and expectations about the quality of care in online forums and on physician rating websites (PRWs). This paper analyzes patient online reviews (PORs) to identify emerging and fading topics and sentiment trends in PRWs during the early stage of the COVID-19 outbreak.

Methods: Text data were collected, including 55,612 PORs of 3430 doctors from three popular PRWs in the United States (RateMDs, HealthGrades, and Vitals) from March 01 to June 27, 2020.

Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs.

The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services.

Converting Visitors of Physicians' Personal Websites to Customers in Online Health Communities: Longitudinal Study.

Background: With the dramatic development of Web 2.0, increasing numbers of patients and physicians are actively involved in online health communities. Despite extensive research on online health communities, the conversion rate from visitor to customer and its driving factors have not been discussed.

How Could Peers in Online Health Community Help Improve Health Behavior.

Human behavior is the largest source of variance in health-related outcomes, and the increasingly popular online health communities (OHC) can be used to promote healthy behavior and outcomes. We explored how the social influence (social integration, descriptive norms and social support) exerted by online social relationships does affect the health behavior of users. Based on an OHC, we considered the effect of three types of social relationships (friendship, mutual support group and competing group) in the OHC.

Exploring the impact of online information signals in leveraging the economic returns of physicians.

Introduction: With the growth in Internet technology, online rating websites encourage patients to contribute actively in rating their physicians. These rating sites provide more information for patients, such as electronic word of mouth (eWOM) and physician trustworthiness. Although several studies in e-commerce have investigated the role of eWOM and seller trustworthiness in the consumer purchase decision-making process and the price premium for products or services, studies on the role of different information sources that reflect the service quality and delivery process in choosing a competent physician remain scarce.

Provision of Paid Web-Based Medical Consultation in China: Cross-Sectional Analysis of Data From a Medical Consultation Website.

Background: Web-based medical consultation, which has been adopted by patients in many countries, requires a large number of doctors to provide services. However, no study has provided an overall picture of the doctors who provide online services.

Objective: This study sought to examine doctors' participation in medical consultation practice in an online consultation platform.

How users adopt healthcare information: An empirical study of an online Q&A community.

Objectives: The emergence of social media technology has led to the creation of many online healthcare communities, where patients can easily share and look for healthcare-related information from peers who have experienced a similar problem. However, with increased user-generated content, there is a need to constantly analyse which content should be trusted as one sifts through enormous amounts of healthcare information. This study aims to explore patients' healthcare information seeking behavior in online communities.