By using a quantitative approach, this study examines the applicability of data mining technique to discover knowledge from open data related to Taiwan's dengue epidemic. We compare results when Google trend data are included or excluded. Data sources are government open data, climate data, and Google trend data. Research findings from analysis of 70,914 cases are obtained. Location and time (month) in open data show the highest classification power followed by climate variables (temperature and humidity), whereas gender and age show the lowest values. Both prediction accuracy and simplicity decrease when Google trends are considered (respectively 0.94 and 0.37, compared to 0.96 and 0.46). The article demonstrates the value of open data mining in the context of public health care.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.actatropica.2018.03.017 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Measuring the impact of a catchment area surveillance tool on cancer center adopters.
Cancer
January 2025
Markey Cancer Center, University of Kentucky, Lexington, Kentucky, USA.
Background: The University of Kentucky Markey Cancer Center developed the data gathering and visualization platform Cancer InFocus (CIF) as a solution for cancer center catchment area surveillance. CIF was released in June 2022 and made available for use to other institutions through a no-cost licensing agreement. The purpose of this study was to evaluate the impact CIF has had on cancer centers since its release.
View Article and Find Full Text PDFThe Continuous Fragility Index of Statistically Significant Findings in Studies Based on High Levels of Evidence Comparing Interventions for Femoroacetabular Impingement Syndrome.
Am J Sports Med
January 2025
Department of Orthopaedics, Rush University Medical Center, Rush University Medical College, Chicago, Illinois, USA.
Background: Critical analysis of studies with high level of evidence has relied on the significance set by the reported values. However, this strategy steers readers toward categorical interpretation of the data; therefore, a more comprehensive approach of data analysis is warranted. The continuous fragility index (CFI) allows for frailty interpretation of any given study's continuous outcome results.
View Article and Find Full Text PDFCognitive Behavioural Therapy to Optimize Post-Operative Recovery (COPE): a randomized controlled feasibility trial in extremity fracture patients.
Pilot Feasibility Stud
January 2025
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Article Synopsis
- The study aimed to assess the feasibility of an online cognitive behavioral therapy (CBT) program for patients recovering from extremity fractures compared to usual care.
- Despite enrolling 94 participants, recruitment showed a "yellow light," indicating some success but needing improvements.
- Compliance with the CBT program was low, as only 60% completed all modules, leading to a "red light," suggesting significant changes are necessary before a larger trial can proceed.
Multi-disciplinary treatment of broncho-esophageal fistula in a high-risk single-lung patient.
J Cardiothorac Surg
January 2025
Section of Cardiothoracic Surgery, Department of Heart Disease, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway.
Background: A broncho-esophageal fistula (BEF) is a medical and surgical disaster. Treatment of BEF is often limited to palliative stent treatment that may migrate or cause erosions and tissue necrosis. Surgical repair of BEF is the only established definite treatment.
View Article and Find Full Text PDFDropping out of a peripartum depression mHealth study: participants' motives and suggestions for improvement.
BMC Med Res Methodol
January 2025
Department of Women's and Children's Health - Obstetric & Reproductive Health Research, Uppsala University, Uppsala, 751 85, Sweden.
Background: Peripartum depression is a common but potentially debilitating pregnancy complication. Mobile applications can be used to collect data throughout the pregnancy and postpartum period to improve understanding of early risk indicators.
Aim: This study aimed to improve understanding of why women drop out of a peripartum depression mHealth study, and how we can improve the app design.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!