机器学习和可解释的人工智能在肠道微生物组研究中的使用:一个范围审查
IEEE journal of biomedical and health informatics
|October 9, 2025
概括
机器学习和人工智能增强了对人类健康的肠道微生物组研究. 可解释的人工智能集成对于可靠的精准医学至关重要,尽管可重复性需要更多的关注.
科学领域:
- 微生物组研究的研究.
- 医学中的人工智能
- 精准医学是一门精准的医学.
背景情况:
- 肠道微生物组研究越来越多地利用机器学习 (ML) 和人工智能 (AI) 来进行复杂的数据分析.
- 可解释的人工智能 (XAI) 对临床应用的透明度和信任至关重要,特别是在精准医学中.
- 将XAI与肠道微生物群数据集成为更可靠的临床见解提供了潜在的潜力.
研究的目的:
- 审查ML和XAI在肠道微生物组研究中的应用.
- 识别知识缺口,并建议未来的研究方向.
- 评估现场可解释性和可重现性的现状.
主要方法:
- 从2018-2024年对出版物的范围审查.
- 在PubMed和Scopus数据库中进行了搜索.
- 选择了76个相关出版物进行分析.
主要成果:
- 在肠道微生物组研究中,ML和AI被广泛应用,如Random Forest等"黑子"模型的流行.
- 该领域的可解释性正在改善,人们对SHAP (SHapley添加式扩展) 等技术越来越感兴趣.
- 研究的可重复性仍然是一个需要更多关注的领域.
结论:
- 在微生物组研究中推进XAI研究对于开发强大的基于微生物组的精密医学至关重要.
- 增加对模型透明度和可重复性的关注将提高临床翻译.
- 在医疗保健中,XAI对释放肠道微生物组数据的全部潜力具有重大前景.
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