大数据分析,人工智能,机器学习和深度学习在孟加拉国医疗保健系统中的影响:范围审查
Md Ashraful Alam1, Md Refat Uz Zaman Sajib2, Fariya Rahman1
1Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh.
Journal of medical Internet research
|October 28, 2024
概括
这篇综述显示,在孟加拉国使用大数据分析 (BDA),人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 的医疗保健研究显著增长. 机器学习模型主导着当前的研究,这表明未来进步的坚实基础.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 公共卫生研究 公共卫生研究
背景情况:
- 大数据分析 (BDA),人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 的快速进步正在改变全球医疗保健.
- 孟加拉国越来越多地在医疗保健和研究中采用这些技术,这导致了相关研究的增加.
- 在了解研究环境,监管挑战以及在孟加拉国卫生部门采用BDA,AI,ML和DL方面存在重大差距.
研究的目的:
- 对孟加拉国医疗保健系统中现有的研究进行范围审查,利用BDA,AI,ML和DL.
- 综合利用这些先进技术在健康和公共卫生研究中的研究结果.
- 确定研究人员在将这些技术整合到医疗保健研究中所面临的局限性.
主要方法:
- 在MEDLINE,IEEE Xplore,Scopus和Embase数据库中搜索2000年1月1日至2023年9月10日之间发表的英语文章.
- 包括使用BDA,AI,ML或DL与健康或公共卫生数据集进行预测或预测的研究,重点是孟加拉国.
- 包括来自同行评审期刊和会议记录的原始研究文章.
主要成果:
- 包括77项研究,过去五年 (2017-2023) 出版量显著增加.
- 主要使用的是机器学习 (ML) 模型 (84%),其次是深度学习 (DL) (9%),人工智能 (AI) (5%),大数据分析 (BDA) (1%).
- 主要研究领域包括非传染性疾病 (30%),传染性疾病 (19%),儿童健康 (14%),精神健康 (12%),利用初级和二级数据.
结论:
- 孟加拉国在医疗保健系统中应用BDA,AI,ML和DL方面取得了重大进展.
- 越来越多的研究强调了人工智能和相关技术在健康研究中的重要性.
- 在孟加拉国,ML模型的普及为增强预测建模和推进数字健康提供了机会.
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