Developing a Quality Evaluation Index System for E-Consultation Doctor-Patient Communication Using the Delphi Method.

Background: E-consultation medical services have become popular globally, which offers patients more options, regardless of time or location. However, research indicates a prevalent issue with the communication quality in e-consultations, leading to sub-optimal patient experiences.

Objective: This study aims to design an evaluation system for e-consultation quality.

Development and evaluation of a nomogram for adverse outcomes of preeclampsia in Chinese pregnant women.

Objective: Preeclampsia, the main cause of maternal and perinatal deaths, is associated with several maternal complications and adverse perinatal outcomes. Some prediction models are uesd to evaluate adverse pregnancy outcomes. However, some of the current prediction models are mainly carried out in developed countries, and many problems are still exist.

Textual Features and Risk Preference Effects on Mental Health Education Among Teenager Students in Chongqing, China.

Background: Mental health is a public health problem of great concern. Previous studies show that textual features and individual psychological characteristics can influence the effect of receiving information.

Purpose: This study explores whether textual features influence the persuasiveness of teenager students' mental health education while considering the influence of risk preference.

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin.

The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data. The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected.

A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population.

We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms. A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets.

Framing Effects and Evidence Type: Influence on the Persuasive Effect of Myopia Prevention Messages Among Elementary School Students in China.

The myopia is a public health issue that attracts much attention. However, limited attention has been paid to the effect of primary school students' acceptance of health messages. Previous studies have found that framing effects and evidence types influence the persuasive effect of messages.

Factors Associated With Influential Health-Promoting Messages on Social Media: Content Analysis of Sina Weibo.

Background: Social media is a powerful tool for the dissemination of health messages. However, few studies have focused on the factors that improve the influence of health messages on social media.

Objective: To explore the influence of goal-framing effects, information organizing, and the use of pictures or videos in health-promoting messages, we conducted a case study of Sina Weibo, a popular social media platform in China.