机器学习应用于被忽视的热带疾病的进展和挑战
ChungYuen Khew1, Rahmad Akbar2, Norfarhan Mohd-Assaad1,3
1Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia.
F1000Research
|July 11, 2025
概括
机器学习可以提高被忽视的热带疾病 (NTD) 的监测,预测和检测. 这项技术有助于发现新的治疗方法,并改善对这些广泛的衰弱性疾病的控制工作.
科学领域:
- 公共卫生 公共卫生
- 计算生物学 计算生物学
- 传染性疾病 传染性疾病
背景情况:
- 被忽视的热带疾病 (NTD) 影响全球17亿人,造成严重的健康问题和经济损失,特别是在发展中国家.
- NTD给患者带来了巨大的财务负担,并降低了劳动生产率,每年耗资数十亿美元.
- 在NTD控制,根除和消除策略方面,迫切需要改进.
研究的目的:
- 审查当前机器学习 (ML) 在对抗NTD方面的应用.
- 确定在NTD监测,预测,检测和治疗中推进ML的挑战和机会.
- 探索ML在发现针对NTD病原体的新疗法方面的潜力.
主要方法:
- 对现有关于机器学习应用在NTD中的研究的文献综述.
- 在监控,预测和检测程序中使用的ML工具的分析.
- 针对导致NTD的病原体进行治疗发现的ML驱动方法的调查.
主要成果:
- 机器学习为增强NTD监测和早期检测提供了有前途的工具.
- 机器学习算法可以改善疾病爆发和传播的预测.
- 审查强调了ML在加速发现新药物和NTD治疗的潜力.
结论:
- 机器学习是推动打击被忽视的热带疾病的关键工具.
- 克服当前的挑战将提高ML在NTD管理和治疗中的作用.
- 将ML纳入公共卫生战略对于控制和可能消除NTD至关重要.
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