Automatic detection of health misinformation: a systematic review

A Systematic Review of Features Forecasting Patient Arrival Numbers

Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely about minimizing the prediction error. Existing models primarily utilize data such as historical patient visits, seasonal trends, holidays, and calendars. However, it is unclear what other features reduce the prediction error. Our systematic literature review identifies studies that use supervised machine learning to predict patient arrival numbers using nontemporal features, which are features not based on time or dates. We scrutinized 26 284 studies, eventually focusing on 27 relevant ones. These studies highlight three main feature groups: weather data, internet search and usage data, and data on (social) interaction of groups. Internet data and social interaction data appear particularly promising, with some studies reporting reduced errors by up to 33%. Although weather data are frequently used, its utility is less clear. Other potential data sources, including smartphone and social media data, remain largely unexplored. One reason for this might be potential data privacy challenges. In summary, although patient arrival prediction has become more important in recent years, there are still many questions and opportunities for future research on the features used in this area.

Analysis of child development facts and myths using text mining techniques and classification models

The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequences, such as inappropriate treatment of children based on myths. While previous research has utilized text-mining techniques to predict child abuse cases, there has been a gap in the analysis of child development myths and facts. This study addresses this gap by applying text mining techniques and classification models to distinguish between myths and facts about child development, leveraging newly gathered data from publicly available websites. The research methodology involved several stages. First, text mining techniques were employed to pre-process the data, ensuring enhanced accuracy. Subsequently, the structured data was analysed using six robust Machine Learning (ML) classifiers and one Deep Learning (DL) model, with two feature extraction techniques applied to assess their performance across three different training-testing splits. To ensure the reliability of the results, cross-validation was performed using both k-fold and leave-one-out methods. Among the classification models tested, Logistic Regression (LR) demonstrated the highest accuracy, achieving a 90 % accuracy with the Bag-of-Words (BoW) feature extraction technique. LR stands out for its exceptional speed and efficiency, maintaining low testing time per statement (0.97 μs). These findings suggest that LR, when combined with BoW, is effective in accurately classifying child development information, thus providing a valuable tool for combating misinformation and assisting parents in making informed decisions.

Sources of information on monkeypox virus infection. A systematic review with meta-analysis

BACKGROUND

Monkeypox (Mpox) virus infection is a topic of growing interest today because of its potential public health impact and concern about possible outbreaks. Reliable and up-to-date sources of information that provide accurate data on its transmission, symptoms, prevention, and treatment are essential for understanding and effectively addressing this disease. Therefore, the aim of the present study is to determine the prevalence of sources of information on Mpox virus infection.

METHODS

An exhaustive systematic review and meta-analysis was carried out using the information available in the PubMed, Scopus, Web of Science, Embase, and ScienceDirect databases up to August 3, 2023. The data were analyzed using R software version 4.2.3. The quality of the cross-sectional studies that formed part of this review was assessed using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) tool. In addition, a subgroup analysis was performed based on the study populations.

RESULTS

Through electronic searches of five databases, a total of 1833 studies were identified. Twenty-four cross-sectional articles were included, with a total sample of 35,959 participants from 34 countries. The pooled prevalence of each of the included information sources was: social networks reached 59% (95% CI: 50-68%; 29,146 participants; 22 studies; I2 = 100%; p < 0.01); the Internet was 61% (95% CI: 44-77%; 14,002 participants; 5 studies; I2 = 100%; p < 0.01), radio reached 10% (95% CI: 07-13%; 8917 participants; 4 studies; I2 = 93%; p < 0.01), television accounted for 24% (95% CI: 09-43%; 14,896 participants; 8 studies; I2 = 100%; p < 0.01), and the combination of radio and television accounted for 45% (95% CI: 31-60%; 4207 participants; 7 studies; I2 = 99%; p < 0.01); for newspapers, it was 15% (95% CI: 05-27%; 2841 participants; 6 studies; I2 = 99%; p < 0.01), friends and relatives accounted for 19% (95% CI: 12-28%; 28,470 participants; 19 studies; I2 = 100%; p < 0.01), the World Health Organization (WHO) accounted for 17% (95% CI: 07-29%; 1656 participants; 3 studies; I2 = 97%; p < 0.01), the Centers for Disease Control and Prevention (CDC) accounted for 10% (95% CI: 03-21%; 2378 participants; 3 studies; I2 = 98%; p < 0.01), and the combination of WHO and CDC websites accounted for 60% (95% CI: 48-72%; 1828 participants; 4 studies; I2 = 96%; p < 0.01), and finally, scientific articles and journals accounted for 24% (95% CI: 16-33%; 16,775 participants; 13 studies; I2 = 99%; p < 0.01).

CONCLUSION

The study suggests that people access a variety of information sources to gain knowledge about Mpox virus infection, with a strong emphasis on online sources such as social networks and the Internet. However, it is important to note that the quality and accuracy of information available from these sources can vary, underscoring the need to promote access to reliable and up-to-date information about this disease to ensure public health.