e.g. mhealth
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Skip search results from other journals and go to results- 4 JMIR Public Health and Surveillance
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Examples of adverse event and illness cluster data sources evaluated by the US Food and Drug Administration Coordinated Outbreak Response & Evaluation (FDA CORE) Network. Multimedia journalism may also be considered a traditional data source in some cases. CFSAN: Center for Food Safety and Applied Nutrition; CDC: Centers for Disease Control and Prevention; USDA: US Department of Agriculture.
JMIR Public Health Surveill 2025;11:e58797
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Cluster analysis was conducted to discern the lockdown measures, the start of vaccination, and changing incidence rates that evoked the greatest levels of interest, in terms of RSV. Third, the general occurrence of clusters was compared between the before and during the pandemic state.
Identification of optimal timing and kind of intervention is essential to protect vulnerable groups during a public health crisis.
Online J Public Health Inform 2024;16:e57718
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Epidemiological Survey of Enterovirus Infections in Taiwan From 2011 to 2020: Retrospective Study
There are sporadic and cluster cases of EV infections in Taiwan, and children have the highest rate of getting EV. The course of the disease often causes severe illness and medical burden [32,33]. Therefore, this study aimed to use the TCDC statistics of communicable diseases and surveillance report to explore the number of EV outpatient and emergency department visits for sporadic and cluster cases from 2011 to 2020.
JMIR Public Health Surveill 2024;10:e59449
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Of the 21,919 trajectories, cluster A contained 1714 (7.82%), cluster B contained 8246 (37.62%), cluster C contained 8376 (38.2%), and cluster D contained 3583 (16.35%).
Weighted spaghetti plot of trajectories assigned to each cluster. The weight (and transparency) of each path represents the number of trajectories following that path. The red line represents the medoid of the cluster. Cluster A=no or low pain, cluster B=mild pain, cluster C=moderate pain, and cluster D=severe pain.
JMIR Mhealth Uhealth 2024;12:e48582
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Additionally, studies have shown that unhealthy lifestyle behaviors tend to cluster, with individuals who engage in 1 unhealthy behavior being more likely to engage in others [18,19]. For example, the co-occurrence of a sedentary lifestyle with excessive substance use, alcohol consumption, and smoking can lead to worse health conditions [18-20], especially for survivors of COVID-19 who are already vulnerable.
JMIR Public Health Surveill 2024;10:e50189
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Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study
K-means clustering is a method that groups data into “k” nonoverlapping, distinct subsets by finding centroids in the data representing each cluster’s center and allocating data points to each cluster by minimizing within-cluster variance around centroids. K-means clustering was used due to its efficiency for small data sets and explainability, aiming to group respondents into clusters based on their clinical features, using the Euclidean distance measure and the Hartigan-Wong algorithm [27].
JMIR Form Res 2024;8:e54747
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Unsupervised machine learning cluster analysis is a methodologic approach that seeks to discover hidden patterns in unlabeled data and can be used to identify distinct subgroups of patients that share certain characteristics that can be tied to specific clinical end points. The primary objective of this approach is to group observations that share similarities in their features or characteristics, allowing the identification of distinct subgroups of patients with similar traits.
JMIR Aging 2023;6:e51844
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As the optimal number of clusters and cutoff values were unknown, cluster evaluation was performed using the silhouette index, which is a popular cluster validity index. Based on the cluster evaluation, the model with the highest silhouette index was selected as the optimal clustering model.
JMIR Mhealth Uhealth 2023;11:e50663
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The EWORS was designed as an early warning system to detect infectious disease outbreaks and guide control practices, so its adaptation in this project as a warning system for areas or wards with a high cluster of TB cases, marking them for follow-up with community ACF, was innovative and novel. The possible effect of the W4 SS TB screening regarding missed cases discussed above was a limitation. Also, the project did not evaluate patients without TB symptoms for TB preventive treatment eligibility.
JMIR Public Health Surveill 2023;9:e40311
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The extracted words were used for cluster analysis. Furthermore, to exclude irrelevant words, we removed words with fewer than 10 occurrences. The dimension of the word vector was set to 100 [32]. This implies that each word was represented as a 1 × 100 vector. Text consisting of N words was represented as a matrix size of N × 100. These word vectors can contain the positional relationship and structural information of each word in the text.
We used the FCM method to cluster the features.
JMIR Form Res 2022;6(11):e38677
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