Incidence and risk factors for congestive heart failure in patients with early breast cancer who received anthracycline and/or trastuzumab: a big data analysis of the Korean Health Insurance Review and Assessment service database

2018 ◽  
Vol 171 (1) ◽  
pp. 181-188 ◽  
Author(s):  
Jung Yoon Choi ◽  
Eun Young Cho ◽  
Yoon Ji Choi ◽  
Jeong Hyeon Lee ◽  
Seung Pil Jung ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Heart Failure ◽  
Congestive Heart Failure ◽  
Big Data ◽  
Health Insurance ◽  
Data Analysis ◽  
Early Breast Cancer ◽  
Big Data Analysis

scholarly journals Hotspot and Frontier Analysis of Exercise Training Therapy for Heart Failure Complicated With Depression Based on Web of Science Database and Big Data Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Yan Wang ◽  
Yuhong Jia ◽  
Molin Li ◽  
Sirui Jiao ◽  
Henan Zhao
Keyword(s):  
Oxidative Stress ◽  
Heart Failure ◽  
Big Data ◽  
Data Analysis ◽  
Exercise Training ◽  
Big Data Analysis ◽  
Hotspot Analysis ◽  
Scientific Publications ◽  
Frontier Analysis ◽  
Simultaneous Intervention

Background: Exercise training has been extensively studied in heart failure (HF) and psychological disorders, which has been shown to worsen each other. However, our understanding of how exercise simultaneously protect heart and brain of HF patients is still in its infancy. The purpose of this study was to take advantage of big data techniques to explore hotspots and frontiers of mechanisms that protect the heart and brain simultaneously through exercise training.Methods: We studied the scientific publications on related research between January 1, 2003 to December 31, 2020 from the WoS Core Collection. Research hotspots were assessed through open-source software, CiteSpace, Pajek, and VOSviewer. Big data analysis and visualization were carried out using R, Cytoscape and Origin.Results: From 2003 to 2020, the study on HF, depression, and exercise simultaneously was the lowest of all research sequences (two-way ANOVAs, p < 0.0001). Its linear regression coefficient r was 0.7641. The result of hotspot analysis of related keyword-driven research showed that inflammation and stress (including oxidative stress) were the common mechanisms. Through the further analyses, we noted that inflammation, stress, oxidative stress, apoptosis, reactive oxygen species, cell death, and the mechanisms related to mitochondrial biogenesis/homeostasis, could be regarded as the primary mechanism targets to study the simultaneous intervention of exercise on the heart and brain of HF patients with depression.Conclusions: Our findings demonstrate the potential mechanism targets by which exercise interferes with both the heart and brain for HF patients with depression. We hope that they can boost the attention of other researchers and clinicians, and open up new avenues for designing more novel potential drugs to block heart-brain axis vicious circle.

scholarly journals TO IMPROVE THE ACCURACY IN IDENTIFYING BREAST CANCER USING VARIOUS TECHNIQUES OF BIG DATA ANALYSIS.

2019 ◽  
Vol 7 (10) ◽  
pp. 491-498
Author(s):  
Aurobind Ganesh ◽  
◽  
K Vijiyakumar ◽  
K Premkumar ◽  
P Mathivanan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis

scholarly journals A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chengjun Zhou ◽  
DuanXu Wang
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Big Data ◽  
Data Analysis ◽  
College Student ◽  
Big Data Analysis ◽  
Technical Support ◽  
Assessment Process ◽  
Student Entrepreneurship ◽  
Study Results

College student entrepreneurship is a complex and dynamic process, in which the potential risks faced by entrepreneurial enterprises are interactive and diverse. The changes in risk assessment for college student entrepreneurship are also dynamic and nonlinear and are affected by many factors, which make the risk assessment process for college student entrepreneurship quite complicated. Big data analysis technology is a new product formed under the background of cloud computing and Internet technology, which has the characteristics of large data scale, multiple data types, and strong data value and provides more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. On the basis of summarizing and analyzing previous research results, this article expounded the research status and significance of the risk assessment algorithm for college student entrepreneurship, elaborated the development background, current status, and future challenges of big data analysis technology, introduced the basic principles of support vector machine (SVM) and hierarchical analytic process, constructed a risk assessment model for college student entrepreneurship based on big data analysis, analyzed the risk factors and assessment indicators of the entrepreneurial model, proposed a risk assessment algorithm for college student entrepreneurship based on big data analysis, performed the discrimination coefficient calculation and comprehensive correlation optimization, and finally conducted a case experiment and its result analysis. The study results show that the risk assessment algorithm for college student entrepreneurship based on big data analysis can effectively realize the comprehensive management of risk factors, make full use of the value of assessment parameter data, and significantly improve the accuracy and efficiency of the risk assessment for college student entrepreneurship, providing more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. The study results of this article provide a reference for further researches on the risk assessment algorithm of college student entrepreneurship based on big data analysis.

scholarly journals Risk of stroke after unilateral or bilateral TKA (simultaneous and staged without discharge) in 327,438 matched patients using data from the National Health Insurance Claims for South Korea

2020 ◽  
Author(s):  
Seung-Beom Han ◽  
Jung-Ro Yoon ◽  
Ji-Young Cheong ◽  
Sang-Soo Lee ◽  
Young-Soo Shin
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Congestive Heart Failure ◽  
Health Insurance ◽  
Connective Tissue ◽  
Incidence Rate ◽  
National Health Insurance ◽  
History Of ◽  
Health Insurance Claims ◽  
Using Data

Abstract Background: Limited data is available regarding the incidence rate and risk factors for stroke associated with unilateral total knee arthroplasty (TKA) and bilateral TKA. This study aims to investigate the incidence rate and risk factors of stroke in patients treated with bilateral TKA compared with patients with unilateral TKA.Methods: In this retrospective nationwide cohort study, we compared patients undergoing unilateral TKA or bilateral TKA using data from the Korean National Health Insurance claims database between January 1, 2009 and August 31, 2017 and included patients older than 40 years of age who underwent primary TKA by the index date as documented primary diagnosis and first additional diagnosis without a history of stroke during the preceding 1 year. We used matched Cox regression models to compare the incidence rate and risk factors of newly acquired stroke among patients treated with unilateral TKA or bilateral TKA after propensity score (PS) matching.Results: In the present study, 163,719 patients who received unilateral TKA were matched to163,719 patients with bilateral TKA (simultaneous and staged without discharge) based on PS. The risk of stroke during the study period was lower in patients treated with bilateral TKA than in patients with unilateral TKA (adjusted hazard ratio [HR] 0.79; P<0.001). Patients who received bilateral TKA were at decreased risk of stroke when the following variables were present: advanced age (70-79 years, HR 0.76; P<0.001), female sex (HR 0.75; P<0.001), rural area (HR 0.77; P<0.001), small- or medium-sized hospital (HR 0.75; P<0.001), health insurance (HR 0.77; P<0.001), history of hypertension drug use (HR 0.75; P<0.001), congestive heart failure (HR 0.70; P=0.032), connective tissue disease (HR 0.71; P=0.01), diabetes (HR 0.77; P<0.001), and diabetes with complication (HR 0.76; P=0.034).Conclusions: The risk of stroke was lower in patients treated with bilateral TKA (simultaneous and staged without discharge) than in patients with unilateral TKA. Patients treated with bilateral TKA were at decreased risk of stroke when the following variables were present: age (70-79 years), female sex, health insurance, history of hypertension drug use, and comorbidities, such as congestive heart failure, connective tissue disease, and diabetes. More importantly, we do state that those with simultaneous bilateral TKA and staged bilateral TKA without discharge could have been healthier. This is precisely what the guidelines implemented by South Korea for patient selection aim to do and our data show that the risk of stroke is not increased in selected patients undergoing SiBTKA and StBTKA without discharge. Therefore, those who underwent 2 unilateral TKAs could have been at more risk of stroke, especially in the 2nd unilateral TKA.

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