机器学习的进步 处理来自疾病诊断传感器的大数据
Shasha Lu1, Jianyu Yang1, Yu Gu1
1School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China.
ACS sensors
|February 16, 2024
概括
准确的,非侵入性疾病诊断传感器至关重要. 机器学习和人工智能 (AI) 是分析这些传感器的大数据的强大工具,可以改善疾病诊断.
科学领域:
- 化学,生物学和医学.
- 生物信息学和人工智能 (AI)
背景情况:
- 准确,非侵入性和廉价的疾病诊断传感器至关重要.
- 生物系统的复杂性和庞大的生物标记数据需要先进的分析工具.
- 机器学习 (ML) 已成为疾病诊断中大数据挖掘的强大方法.
研究的目的:
- 审查大数据收集方法和疾病诊断传感器的基本ML算法.
- 讨论最近在ML和分子计算疾病诊断传感器方面的进展.
- 通过大数据挖掘突出ML在疾病诊断中的机遇和挑战.
主要方法:
- 对生物传感器相关的大数据收集策略的概述.
- 解释用于数据挖掘的基本机器学习算法.
- 关于分子计算疾病诊断中ML应用的最新研究的综合.
主要成果:
- 机器学习模型有效地从疾病诊断传感器中挖掘大数据.
- 人工智能和生物信息学提高了分子计算用于诊断的灵敏度和准确性.
- 目前正在开发模块化工作流程,用于ML驱动的疾病诊断.
结论:
- 机器学习对于通过大数据分析推进疾病诊断传感器至关重要.
- 需要进一步发展,以克服挑战,并充分利用ML来准确诊断疾病.
- 机器学习与分子计算的整合有望带来更敏感,更精确的诊断工具.
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