A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset.

Background: Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups.

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology.

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach.

(1) . Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions.

Exploring Readiness for Birth Control in Improving Women Health Status: Factors Influencing the Adoption of Modern Contraceptives Methods for Family Planning Practices.

Background: Pakistan is the world's sixth most populated country, with a population of approximately 208 million people. Despite this, just 25% of legitimate couples say they have used modern contraceptive methods. A large body of literature has indicated that sexual satisfaction is a complex and multifaceted concept, since it involves physical and cultural components.

Examining Different Factors in Web-Based Patients' Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System.

(1) Background: The appearance of physician rating websites (PRWs) has raised researchers' interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers' information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs' features, the findings of these studies, and peer-reviewed articles.

Exploring the Impact of Linguistic Signals Transmission on Patients' Health Consultation Choice: Web Mining of Online Reviews.

Background: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs).

Methods: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers' decision making.

The Impact of Signals Transmission on Patients' Choice through E-Consultation Websites: An Econometric Analysis of Secondary Datasets.

(1) Background: The COVID-19 pandemic has dramatically and rapidly changed the overall picture of healthcare in the way how doctors care for their patients. Due to the significant strain on hospitals and medical facilities, the popularity of web-based medical consultation has drawn the focus of researchers during the deadly coronavirus disease (COVID-19) in the United States. Healthcare organizations are now reacting to COVID-19 by rapidly adopting new tools and innovations such as e-consultation platforms, which refer to the delivery of healthcare services digitally or remotely using digital technology to treat patients.

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").

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets.

(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.

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.

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.

Fear of Terror and Psychological Well-Being: The Moderating Role of Emotional Intelligence.

The purpose of this study was to empirically explore whether or not the level of emotional intelligence of adolescents mitigates the potential adverse effects of the fear of terror on their psychological well-being. Data for this study were collected through a voluntary survey from a sample of 385 adolescents residing in the terrorism-affected provinces of Pakistan: Khyber Pakhtunkhwa (KPK) and Balochistan. The results from the structural equation modeling revealed that fear of terrorism had a significant negative relationship with the psychological well-being of adolescents.