Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare.

IF 3.8 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Praneel Sharma, Pratyusha Sharma, Kamal Sharma, Vansh Varma, Vansh Patel, Jeel Sarvaiya, Jonsi Tavethia, Shubh Mehta, Anshul Bhadania, Ishan Patel, Komal Shah
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引用次数: 0

Abstract

The term "big data analytics (BDA)" defines the computational techniques to study complex datasets that are too large for common data processing software, encompassing techniques such as data mining (DM), machine learning (ML), and predictive analytics (PA) to find patterns, correlations, and insights in massive datasets. Cardiovascular diseases (CVDs) are attributed to a combination of various risk factors, including sedentary lifestyle, obesity, diabetes, dyslipidaemia, and hypertension. We searched PubMed and published research using the Google and Cochrane search engines to evaluate existing models of BDA that have been used for CVD prediction models. We critically analyse the pitfalls and advantages of various BDA models using artificial intelligence (AI), machine learning (ML), and artificial neural networks (ANN). BDA with the integration of wide-ranging data sources, such as genomic, proteomic, and lifestyle data, could help understand the complex biological mechanisms behind CVD, including risk stratification in risk-exposed individuals. Predictive modelling is proposed to help in the development of personalized medicines, particularly in pharmacogenomics; understanding genetic variation might help to guide drug selection and dosing, with the consequent improvement in patient outcomes. To summarize, incorporating BDA into cardiovascular research and treatment represents a paradigm shift in our approach to CVD prevention, diagnosis, and management. By leveraging the power of big data, researchers and clinicians can gain deeper insights into disease mechanisms, improve patient care, and ultimately reduce the burden of cardiovascular disease on individuals and healthcare systems.

大数据分析在个性化心血管医疗中的革命性应用。
术语“大数据分析(BDA)”定义了研究复杂数据集的计算技术,这些数据集对于普通数据处理软件来说太大了,包括数据挖掘(DM)、机器学习(ML)和预测分析(PA)等技术,以在大规模数据集中发现模式、相关性和洞察力。心血管疾病(cvd)是多种危险因素的综合结果,包括久坐不动的生活方式、肥胖、糖尿病、血脂异常和高血压。我们使用谷歌和Cochrane搜索引擎检索PubMed和发表的研究,以评估已用于CVD预测模型的现有BDA模型。我们批判性地分析了使用人工智能(AI)、机器学习(ML)和人工神经网络(ANN)的各种BDA模型的缺陷和优势。BDA整合了广泛的数据源,如基因组、蛋白质组学和生活方式数据,可以帮助理解心血管疾病背后的复杂生物学机制,包括风险暴露个体的风险分层。提出了预测模型,以帮助开发个性化药物,特别是在药物基因组学;了解遗传变异可能有助于指导药物选择和剂量,从而改善患者的预后。总之,将BDA纳入心血管研究和治疗代表了我们在心血管疾病预防、诊断和管理方法上的范式转变。通过利用大数据的力量,研究人员和临床医生可以更深入地了解疾病机制,改善患者护理,并最终减轻心血管疾病对个人和医疗保健系统的负担。
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来源期刊
Bioengineering
Bioengineering Chemical Engineering-Bioengineering
CiteScore
4.00
自引率
8.70%
发文量
661
期刊介绍: Aims Bioengineering (ISSN 2306-5354) provides an advanced forum for the science and technology of bioengineering. It publishes original research papers, comprehensive reviews, communications and case reports. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. All aspects of bioengineering are welcomed from theoretical concepts to education and applications. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, four key features of this Journal: ● We are introducing a new concept in scientific and technical publications “The Translational Case Report in Bioengineering”. It is a descriptive explanatory analysis of a transformative or translational event. Understanding that the goal of bioengineering scholarship is to advance towards a transformative or clinical solution to an identified transformative/clinical need, the translational case report is used to explore causation in order to find underlying principles that may guide other similar transformative/translational undertakings. ● Manuscripts regarding research proposals and research ideas will be particularly welcomed. ● Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. ● We also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds. Scope ● Bionics and biological cybernetics: implantology; bio–abio interfaces ● Bioelectronics: wearable electronics; implantable electronics; “more than Moore” electronics; bioelectronics devices ● Bioprocess and biosystems engineering and applications: bioprocess design; biocatalysis; bioseparation and bioreactors; bioinformatics; bioenergy; etc. ● Biomolecular, cellular and tissue engineering and applications: tissue engineering; chromosome engineering; embryo engineering; cellular, molecular and synthetic biology; metabolic engineering; bio-nanotechnology; micro/nano technologies; genetic engineering; transgenic technology ● Biomedical engineering and applications: biomechatronics; biomedical electronics; biomechanics; biomaterials; biomimetics; biomedical diagnostics; biomedical therapy; biomedical devices; sensors and circuits; biomedical imaging and medical information systems; implants and regenerative medicine; neurotechnology; clinical engineering; rehabilitation engineering ● Biochemical engineering and applications: metabolic pathway engineering; modeling and simulation ● Translational bioengineering
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