Qalbi: Cardiovascular Disease (CVD) Dataset

Published: 29 January 2025| Version 1 | DOI: 10.17632/d99rf6wt5s.1
Contributors:
, Rashid Mehmood,
,

Description

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.

Files

Steps to reproduce

Please see [1].

Institutions

King Abdulaziz University, Islamic University of Madinah Faculty of Engineering, King Khalid University

Categories

Natural Language Processing, Cardiovascular Disease, Big Data Analytics, Smart City, Pervasive Healthcare, Deep Learning, Scientific Services, Bidirectional Encoder Representations From Transformers

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