Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.
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http://dx.doi.org/10.1007/s12652-020-02617-4 | DOI Listing |
PLoS One
January 2025
Department of Anesthesiology, Henan Provincial Chest Hospital & Chest Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background: Postoperative nausea and vomiting (PONV) is a common complication of general anesthesia. This affects 30-80% of patients, and leads to discomfort and extended hospital stays. The effectiveness of penehyclidine for preventing PONV remains a subject of debate in the literature.
View Article and Find Full Text PDFPLoS One
January 2025
Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Gangshan Hospital, Kaohsiung, Taiwan.
Background/purpose: Dyslipidemia, a hallmark of metabolic syndrome (MetS), contributes to atherosclerotic and cardiometabolic disorders. Due to days-long analysis, current clinical procedures for cardiotoxic blood lipid monitoring are unmet. This study used AI-assisted attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to identify MetS and precisely quantify multiple blood lipid levels with a blood sample of 0.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
School of Software, Taiyuan University of Technology, Taiyuan, China.
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Purpose: To investigate the heritability of genetic influence on macular choroidal vascularity index (CVI).
Methods: Total choroidal area (TCA), luminal area (LA), and CVI was measured using horizontal scan of spectral-domain optical coherence tomography with enhanced depth imaging in the 373 healthy twin participants. Characteristics of the participants were investigated, including age, sex, axial length, hypertension, diabetes, drinking habits, and smoking status.
PLoS One
January 2025
School of Architecture and Urban Planning, Lanzhou Jiaotong University, Gansu, Lanzhou, China.
The built environment is an important determinant of travel demand and mode choice. Studying the relationship between the built environment and transportation usage can support and assist traffic policy interventions. Previous studies often assumed that this relationship is linear; however, the impact of the built environment on non-motorized travel efficiency may be more complex than the typically modeled linear relationships.
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