Qalbi: Cardiovascular Disease (CVD) Dataset

The Cardiovascular Disease (CVD) Dataset was curated from the widely recognized Scopus academic database. It includes data about 43,398 English research articles related to CVD published in 2022, focused on the academic disciplines of Computer Science and Medicine. The dataset is restricted to journal articles and includes key attributes: Title, Year, DOI, Abstract, Authors, and Keywords. This dataset was developed to identify parameters from Scopus and extract detailed taxonomies, offering insights into academic perspectives on CVD [1]. The dataset and its analysis are integral to our broader research and development strategy, focusing on multiperspective parameter discovery and the advancement of autonomous systems [2]. Our approach leverages big data, deep learning, and digital media to explore and analyze cross-sectional, multi-perspective insights, supporting improved decision-making and more effective governance frameworks. These perspectives span academic, public, industrial, and governmental domains. We have applied this approach across various fields and sectors, including AI explainability and governance [3], [4], energy [5], education [6], healthcare [7]–[9], transportation [10], [11], labor markets [12], [13], tourism [14], service industries [15], and others. References [1] doi: 10.2139/SSRN.5086729. [2] doi: 10.54377/95e5-08b3 [3] doi: 10.3389/FNINF.2024.1472653/BIBTEX. [4] doi: 10.2139/SSRN.5086713. [5] doi: 10.3389/FENRG.2023.1071291. [6] doi: 10.3389/FRSC.2022.871171/BIBTEX. [7] doi: 10.3390/SU14063313. [8] doi: 10.3390/TOXICS11030287. [9] doi: 10.3390/app10041398. [10] doi: 10.3390/SU14095711. [11] doi: 10.3390/s21092993. [12] doi: 10.3390/JOURNALMEDIA4010010. [13] doi: 10.1177/00368504231213788. [14] doi: 10.3390/SU15054166. [15] doi: 10.3390/SU152216003.本心血管疾病（Cardiovascular Disease, CVD）数据集源自广受认可的Scopus学术数据库，共收录2022年发表的43398篇与CVD相关的英文学术论文，研究领域聚焦于计算机科学与医学。本数据集仅包含期刊论文，核心字段包括标题、发表年份、数字对象标识符（Digital Object Identifier, DOI）、摘要、作者与关键词。 本数据集旨在从Scopus数据库中提取相关参数并构建详细分类体系，以期为心血管疾病领域的学术研究视角提供洞见[1]。本数据集及其分析工作是我们整体研发战略的核心组成部分，该战略聚焦多视角参数挖掘与自主系统的研发升级[2]。我们的研究方法依托大数据、深度学习与数字媒体技术，开展跨领域、多视角的探索与分析，为优化决策制定与完善治理框架提供支撑。上述研究视角覆盖学术、公众、工业与政府四大领域。我们已将该研究方法应用于多个领域与行业，包括AI可解释性与治理[3][4]、能源[5]、教育[6]、医疗健康[7]–[9]、交通运输[10][11]、劳动力市场[12][13]、旅游业[14]、服务行业[15]等诸多方向。 参考文献 [1] doi: 10.2139/SSRN.5086729. [2] doi: 10.54377/95e5-08b3 [3] doi: 10.3389/FNINF.2024.1472653/BIBTEX. [4] doi: 10.2139/SSRN.5086713. [5] doi: 10.3389/FENRG.2023.1071291. [6] doi: 10.3389/FRSC.2022.871171/BIBTEX. [7] doi: 10.3390/SU14063313. [8] doi: 10.3390/TOXICS11030287. [9] doi: 10.3390/app10041398. [10] doi: 10.3390/SU14095711. [11] doi: 10.3390/s21092993. [12] doi: 10.3390/JOURNALMEDIA4010010. [13] doi: 10.1177/00368504231213788. [14] doi: 10.3390/SU15054166. [15] doi: 10.3390/SU152216003.