从字节到脏:人工智能在糖尿病病中的旅程
Debargha Basuli1, Akil Kavcar2, Sasmit Roy3
1Department of Nephrology & Hypertension, Brody School of Medicine, East Carolina University, 2355 W Arlington Blvd, Greenville, NC, 27834, USA. basulid17@ecu.edu.
Journal of nephrology
|August 12, 2024
概括
人工智能 (AI) 和机器学习有效地预测糖尿病病 (DKD) 的进展,优于传统的风险评分. 未来的人工智能工具有望实现个性化的DKD管理和新药开发.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 内分泌学 在内分泌学.
- 生物医学信息学 生物医学信息学
背景情况:
- 糖尿病病 (DKD) 是2型糖尿病的一个主要并发症,需要早期检测才能有效管理患者.
- 人工智能 (AI) 为识别和预测DKD提供了有希望的解决方案.
- 机器学习 (ML) 模型越来越多地与临床数据一起用于DKD预测.
研究的目的:
- 审查当前AI和ML在糖尿病病中的应用.
- 检查有关风险评分和ML方法的文献,以预测DKD发展.
- 探索AI在DKD研究中的潜力,超出预测.
主要方法:
- 在Medline (PubMed),Google Scholar和Scopus数据库中进行了全面的文献搜索,截至2023年7月.
- 专注于人工智能在DKD预测和进展中的作用的研究是使用相关关键词提取的.
- 分析了对DKD人工智能和机器学习模型的现有文献.
主要成果:
- 与传统的风险评分模型相比,AI和ML模型在预测DKD进展方面表现优越.
- 在DKD中人工智能应用范围扩展到整合遗传/表观遗传数据和个性化治疗.
- 挑战包括对大数据集的需求和DKD人工智能工具的标准化.
结论:
- 人工智能和机器学习具有改变糖尿病病管理和患者护理的巨大潜力.
- 人工智能驱动的工具可以克服传统方法的局限性,并提高对DKD的理解.
- 未来的研究必须解决当前的挑战,以促进AI在DKD的临床实施.
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