使用机器学习模型来规划艾滋病毒服务:设计,实施和评估中的新兴机会
T Dzinamarira1, E Mbunge2, I Chingombe3
1School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa. u19395419@up.ac.za.
概括
机器学习为改善艾滋病毒/艾滋病预防和治疗服务提供了新的途径. 探索这些人工智能技术可以帮助克服现有的障碍,实现全球艾滋病毒/艾滋病目标.
科学领域:
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
背景情况:
- 艾滋病毒/艾滋病是全球主要的健康和经济负担,影响3600万人.
- 尽管取得了进展,但仍存在重大障碍,阻碍实现联合国艾滋病规划署95-95-95目标,包括检测,获得治疗和提供服务.
- 弱势群体在艾滋病毒/艾滋病预防和护理方面面临着特殊的挑战.
研究的目的:
- 探索机器学习 (ML) 技术在提高艾滋病毒/艾滋病服务设计,预测,实施和评估方面的潜力.
- 确定创新的分析策略,以改善艾滋病毒/艾滋病的预防,治疗和认识.
主要方法:
- 从2022年10月24日至11月5日进行了快速文献审查.
- 在主要的电子数据库 (PubMed,Google Scholar,Scopus等) 进行了搜索. 使用多种不同的关键词.
- 应用了包含和排除标准来选择相关的出版物进行审查.
主要成果:
- 审查确定了在理解和改进艾滋病毒/艾滋病服务方面应用ML的新兴机会.
- ML有可能解决HIV服务设计,规划和评估中的挑战.
- 该研究强调需要创新的分析策略来加强艾滋病毒/艾滋病的干预.
结论:
- 机器学习为推动艾滋病毒/艾滋病研究和干预提供了一个有希望的途径.
- 整合ML可以导致更有效的艾滋病毒/艾滋病预防,治疗和宣传策略.
- 进一步探索ML对于克服现有的障碍和实现全球HIV/AIDS目标至关重要.
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