大数据和机器学习时代的非侵入性生物标志物
Konstantinos Lazaros1, Styliani Adam1, Marios G Krokidis1
1Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
非侵入性生物标志物使用可访问的样本和数据提供更安全,更具成本效益的疾病诊断. 先进的计算方法提高了对个性化医疗的准确性,克服了传统的局限性.
科学领域:
- 生物标志物发现发现
- 诊断技术 诊断技术 诊断技术
- 计算生物学 计算生物学
背景情况:
- 侵入性诊断方法带来了诸多挑战,包括高成本,风险和患者的不适.
- 非侵入性生物标志物利用可访问的样本 (血液,唾液,呼吸) 和可穿戴数据进行疾病洞察.
- 与传统方法相比,现有的非侵入性方法在敏感性和特异性方面存在局限性.
研究的目的:
- 审查最近在非侵入性生物标记技术方面的创新.
- 检查这些新兴诊断工具的临床应用和局限性.
- 突出非侵入性生物标志物在精确诊断中的不断变化的作用.
主要方法:
- 关于非侵入性诊断技术的当前文献的综述.
- 分子和成像方法的分析,包括呼吸学和微生物群的分析.
- 在生物标志物分析中探索人工智能和机器学习应用.
主要成果:
- 非侵入性生物标志物通过安全和可访问的手段提供精确的诊断信息.
- 呼吸学,肠道微生物组分析和可穿戴技术的进步扩大了诊断能力.
- 人工智能和机器学习通过分析复杂的多模式数据显著提高了诊断准确性.
结论:
- 非侵入性生物标志物是侵入性技术的变革性替代品,提供更好的安全性和成本效益.
- 计算进步对于克服敏感性和特异性挑战至关重要.
- 非侵入性生物标志物即将成为大规模精确诊断和个性化医学的基石.
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