Kumari Juli, Kumar Ela, Kumar Deepak
Indira Gandhi Delhi Technical University for Women (IGDTUW), New Church Rd, Kashmere Gate, Delhi, James Church, New Delhi, 110006 India.
Center of Excellence in Weather & Climate Analytics, Atmospheric Sciences Research Center (ASRC), University at Albany (UAlbany), State University of New York (SUNY), Albany, New York 12226 USA.
Arch Comput Methods Eng. 2023 Mar 31:1-29. doi: 10.1007/s11831-023-09915-y.
Machine and deep learning are used worldwide. Machine Learning (ML) and Deep Learning (DL) are playing an increasingly important role in the healthcare sector, particularly when combined with big data analytics. Some of the ways that ML and DL are being used in healthcare include Predictive Analytics, Medical Image Analysis, Drug Discovery, Personalized Medicine, and Electronic Health Records (EHR) Analysis. It has become one of the advanced and popular tool for computer science domain.' The advancement of ML and DL for various fields has opened new avenues for research and development. It could revolutionize prediction and decision-making capabilities. Due to increased awareness about the ML and DL in the healthcare, it has become one of the vital approaches for the sector. High-volume of unstructured, and complex medical imaging data from health monitoring devices, gadgets, sensors, etc. Is the biggest trouble for healthcare sector. The current study uses analysis to examine research trends in adoption of machine learning and deep learning approaches in the healthcare sector. The WoS database for SCI/SCI-E/ESCI journals are used as the datasets for the comprehensive analysis. Apart from these various search strategy are utilised for the requisite scientific analysis of the extracted research documents. Bibliometrics R statistical analysis is performed for year-wise, nation-wise, affiliation-wise, research area, sources, documents, and author based analysis. VOS viewer software is used to create author, source, country, institution, global cooperation, citation, co-citation, and trending term co-occurrence networks. ML and DL, combined with big data analytics, have the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and accelerating the development of new treatments, so the current study will help academics, researchers, decision-makers, and healthcare professionals understand and direct research.
机器学习和深度学习在全球范围内得到应用。机器学习（ML）和深度学习（DL）在医疗保健领域发挥着越来越重要的作用，特别是与大数据分析相结合时。ML和DL在医疗保健中的一些应用方式包括预测分析、医学图像分析、药物发现、个性化医疗和电子健康记录（EHR）分析。它已成为计算机科学领域先进且流行的工具之一。ML和DL在各个领域的进步为研发开辟了新途径。它可能会彻底改变预测和决策能力。由于医疗保健领域对ML和DL的认识提高，它已成为该领域的重要方法之一。来自健康监测设备、小工具、传感器等的大量非结构化和复杂的医学影像数据是医疗保健领域最大的难题。当前的研究使用分析方法来研究医疗保健领域采用机器学习和深度学习方法的研究趋势。将SCI/SCI-E/ESCI期刊的WoS数据库用作综合分析的数据集。除了这些，还利用各种搜索策略对提取的研究文档进行必要的科学分析。进行文献计量学R统计分析，包括逐年、按国家、按机构、研究领域、来源、文档和作者的分析。使用VOS viewer软件创建作者、来源、国家、机构、全球合作、引用、共引和趋势术语共现网络。ML和DL与大数据分析相结合，有可能通过改善患者预后、降低成本和加速新疗法的开发来彻底改变医疗保健，因此当前的研究将有助于学者、研究人员、决策者和医疗保健专业人员理解并指导研究。
