基于机器学习和深度学习的智能医疗方法:最新进展、应用、挑战和机遇。

IF 3.1 Q2 HEALTH CARE SCIENCES & SERVICES
AIMS Public Health Pub Date : 2024-01-05 eCollection Date: 2024-01-01 DOI:10.3934/publichealth.2024004
Anichur Rahman, Tanoy Debnath, Dipanjali Kundu, Md Saikat Islam Khan, Airin Afroj Aishi, Sadia Sazzad, Mohammad Sayduzzaman, Shahab S Band
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引用次数: 0

摘要

近年来,机器学习(ML)和深度学习(DL)已成为解决智能医疗应用中各种挑战(如疾病预测、药物发现、医学图像分析等)的主要方法。此外,鉴于目前在 ML 和 DL 领域取得的进展,这两种方法在医疗保健领域提供支持的潜力巨大。本研究对用于医疗保健系统的 ML 和 DL 进行了详尽的调查,主要集中在重要的技术特点、集成优势、应用、前景和未来指导方针等方面。为了开展研究,我们使用不同的关键词找到了最著名的期刊和会议数据库,以发现学术成果。首先,我们以汇编的方式提供了智能医疗领域基于 ML-DL 分析的最新进展。其次,我们整合了 ML 和 DL 的各种服务进展,包括 ML-医疗保健、DL-医疗保健和 ML-DL- 医疗保健。然后,我们提出了基于 ML 和 DL 的医疗行业应用。最后,我们根据观察结果强调了研究争议和进一步研究的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.

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来源期刊
AIMS Public Health
AIMS Public Health HEALTH CARE SCIENCES & SERVICES-
CiteScore
4.80
自引率
0.00%
发文量
31
审稿时长
4 weeks
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