The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.

Introduction: This research investigates the epidemiology, underlying mechanisms, and prevention strategies associated with occupational noise-induced hearing loss (ONIHL), while also presenting effective control measures. Source of data: Relevant literature was sourced from databases such as MEDLINE, PubMed, Embase, Web of Science, and Google Scholar, encompassing articles until February 2025. Areas of agreement: ONIHL is widely recognized as a common occupational health issue among workers. Additionally, noise can lead to psychological and physiological complications beyond direct auditory damage. Areas of controversy: The effects of noise differ across individuals, and the precise causes behind ONIHL remain poorly understood, although several pathways have been proposed. Growing points: New treatment options focused on oxidative stress, inflammation, and neuropathy are being developed through innovative drug delivery systems. Areas timely for developing research: There is a pressing need for molecular approaches to explore the mechanisms of ONIHL, particularly in the care of individuals with hearing disabilities.

Breast cancer is the most common cancer in women worldwide; thus, the prolongation of survival, and the incidence and risk factors, including radiotherapy, for developing secondary malignancies are important. We compared the incidence of secondary and new primary cancers in women with breast cancer (CA^Pos) and well-matched for age, geographic region, and monthly income cancer-free controls (CA^Neg). The risk for secondary cancers with and without radiotherapy was also compared in CA^Pos women. We enrolled 2422 CA^Pos patients and CA^Neg 12,110 controls. In a 4-year follow-up, the secondary cancers risk was significant in the CA^Pos group (adjusted hazard ratio [AHR]: 1.59; 95% confidence interval [CI]: 1.17–2.18). Only the risk of uterine cancer was significant compared with the controls (AHR: 6.30; 95% CI: 2.28–17.38). CA^Pos patients and <50 years old had a higher risk for secondary cancers. Developing secondary cancers was significant in the first follow-up year (AHR: 1.51; 95% CI: 1.11–2.06). Radiotherapy had no significant effect on the CA^Pos group, but it was significant (P = 0.0298) in women ≥60 years old (elderly). We recommend monitoring secondary cancers in CA^Pos women, especially those <50 years old, and during the first year of follow-up. Radiotherapy should be used more carefully in elderly CA^Pos women.

Background: Breast cancer (BC) is the most common malignant cancer in women. A predictive model is required to predict the 5-year survival in patients with BC (5YSPBC) and improve the treatment quality by increasing their survival rate. However, no reports in literature about apps developed and designed in medical practice to classify the 5YSPBC. This study aimed to build a model to develop an app for an automatically accurate classification of the 5YSPBC. Methods: A total of 1810 patients with BC were recruited in a hospital in Taiwan from the secondary data with codes on 53 characteristic variables that were endorsed by professional staff clerks as of December 31, 2019. Five models (i.e., revolution neural network [CNN], artificial neural network, Naïve Bayes, K-nearest Neighbors Algorithm, and Logistic regression) and 3 tasks (i.e., extraction of feature variables, model comparison in accuracy [ACC] and stability, and app development) were performed to achieve the goal of developing an app to predict the 5YSPBC. The sensitivity, specificity, and receiver operating characteristic curve (area under ROC curve) on models across 2 scenarios of training (70%) and testing (30%) sets were compared. An app predicting the 5YSPBC was developed involving the model estimated parameters for a website assessment. Results: We observed that the 15-variable CNN model yields higher ACC rates (0.87 and 0.86) with area under ROC curves of 0.80 and 0.78 (95% confidence interval 0.78–82 and 0.74–81) based on 1357 training and 540 testing cases an available app for patients predicting the 5YSPBC was successfully developed and demonstrated in this study. Conclusion: The 15-variable CNN model with 38 parameters estimated using CNN for improving the ACC of the 5YSPBC has been particularly demonstrated in Microsoft Excel. An app developed for helping clinicians assess the 5YSPBC in clinical settings is required for application in the future.

In the context of climate change and global warming, heat-related illness (HRI) is anticipated to escalate and become a major concern. Patients with severe HRI primarily present to the emergency department (ED), but there are no prediction tools for mortality in HRI patients who visit ED. The objective of this study was to use machine learning approaches to establish prediction models for mortality in patients with HRI who visit ED. We included all patients aged 20 and above with a final diagnosis of HRI who visited the EDs of three hospitals (Chi Mei Medical Center, Chi Mei Hospital Liouying, and Chi Mei Hospital Chiali) between January 2010 and October 2021. Patients who had transferred to other hospitals or had insufficient data were excluded. A total of 11 predictive feature variables were used in the algorithms. The primary outcome was in-hospital mortality or impending death discharge. We used machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost) to establish prediction models for mortality in such patients. Accuracy, sensitivity, specificity, and area under curve (AUC) were used as indicators to evaluate the performance of prediction models. Out of the 820 HRI patients included in the analysis, 1.5% had mortality. All six prediction models had a high AUC, ranging from 0.825 to 0.991, and LightGBM which included peripheral oxygen saturation (SpO₂₎ and Glasgow Coma Scale (GCS) score on arrival as the two main features had the highest AUC. The accuracy, sensitivity, and specificity of LightGBM were 0.976, 1.000 and 0.975, respectively. Machine learning-based prediction models are promising tools in accurately predicting mortality in HRI patients who present to the ED.