人工智能和机器学习在预测和打击抗菌素耐药性的作用
Hazrat Bilal1, Muhammad Nadeem Khan2, Sabir Khan3
1Jiangxi Key Laboratory of oncology (2024SSY06041), JXHC Key Laboratory of Tumour Metastasis, NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Jiangxi Cancer Hospital & Institute, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi 330029, PR China.
人工智能和机器学习 (AI/ML) 在对抗抗菌素耐药性 (AMR) 方面表现有前途. 这些技术可以预测疫情,发现新药,并指导抗生素处方,以改善全球公共卫生.
科学领域:
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球重大公共卫生威胁.
- 开发新的策略对于减轻AMR日益增长的影响至关重要.
研究的目的:
- 审查人工智能和机器学习 (AI/ML) 在打击AMR方面的作用.
- 在AMR预测,药物发现和临床决策支持中探索AI/ML应用.
主要方法:
- 关于AMR中AI/ML应用的当前文献的综合.
- 分析各种AI/ML技术,包括监督,无监督,深度学习,强化学习和NLP.
- 检查各种数据来源,如临床,基因组,微生物组和流行病学数据.
主要成果:
- 人工智能/ML模型在使用各种数据集预测AMR爆发时表现出高准确度.
- 人工智能/ML促进了新型抗微生物药物的发现,药物重新定位和组合疗法.
- 基于人工智能的临床决策支持系统增强了抗生素处方实践和实时指导.
- 人工智能有助于改善AMR监测,抗药性趋势分析和早期疫情检测.
结论:
- 通过先进的数据分析和预测建模,AI/ML提供了强大的工具来打击抗微生物耐药性.
- 尽管存在诸如数据隐私和模型偏见等挑战,但对于未来的AMR缓解策略来说,持续的AI/ML进步至关重要.
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