检测未被发现的:机器学习在早期疾病诊断中
Kanika Rathi1, Sakshi Sharma1, Anil Barnwal1
1Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India.
Basic & clinical pharmacology & toxicology
|September 4, 2025
概括
机器学习 (ML) 模型正在通过在症状出现之前识别标记来彻底改变早期疾病检测. 这种方法通过及时的干预和改善患者的结果来提高医疗保健.
科学领域:
- 医疗信息学
- 医疗保健中的人工智能
- 计算生物学
背景情况:
- 早期发现疾病对于有效的医疗干预和改善患者的结果至关重要.
- 机器学习提供了强大的工具来识别疾病标志物,
- 包括深度学习在内的机器学习的进步正在改变诊断能力.
研究的目的:
- 为早期疾病检测提供ML应用的全面概述.
- 探索各种ML方法,它们的评估指标和实际应用.
- 讨论将机器学习纳入临床实践的挑战和未来方向.
主要方法:
- 对监督学习 (SVM,决策树,随机森林) 和无监督学习 (K-means,聚类,PCA) 算法的审查.
- 探索深度学习架构 (CNN,RNN,变压器) 和强化学习.
- 分析瘤学,心脏病学,神经学和传染病中的实际应用.
主要成果:
- 多种医学领域的ML模型在检测早期疾病标志物方面具有显著的潜力.
- 高质量的数据,均衡的分布和临床相关性对于成功的ML实施至关重要.
- 主要挑战包括数据稀缺性,可解释性,隐私问题和临床整合.
结论:
- 数据科学家和临床医生之间需要密切合作才能成功地将ML转化为医疗保健.
- 未来的方向包括可解释的人工智能,联合学习,多式数据融合和量子机器.
- 持续的研究和开发对于通过ML提前发现疾病至关重要.
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