探索经典机器学习用于识别病理性肺部听觉
Haroldas Razvadauskas1, Evaldas Vaičiukynas2, Kazimieras Buškus2
1Lithuanian University of Health Sciences, Kaunas, Lithuania.
Computers in biology and medicine
|December 2, 2023
概括
机器学习准确地区分正常和异常的肺部声音,使用数字听力镜数据. 监督模型,特别是随机森林,在改善临床环境中的诊断准确性方面显示出希望.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 肺部医学 肺部医学
背景情况:
- 机器学习 (ML) 在生物医学研究中的应用正在迅速扩大.
- 尽管数字耳镜和人工智能技术取得了进展,但肺听分析的临床应用仍然有限.
- 目前用于肺部疾病的诊断方法往往缺乏准确性,影响患者的护理.
研究的目的:
- 应用ML技术来分析肺听信号.
- 使用音频功能来区分正常和异常的肺部声音.
- 在这个诊断任务中评估各种ML算法的有效性.
主要方法:
- 利用了来自45名患者的数字6通道听觉.
- 使用Python库Surfboard提取的音频特征 (例如,F0-4,声音强度,HNR,DFA,日志能量,RMS,MFCC).
- 应用无监督 (公平砍伐森林,异常森林) 和监督 (随机森林,正规化后勤回归) 的ML模型与数据预处理技术.
主要成果:
- 监督的ML模型的性能优于无监督的方法.
- 随机森林的平均AUCROC为0.691 (准确率71.11%) 的侧面检测和0.721 (准确率68.89%) 的基于患者的检测.
- 通过平均化输出的决策融合提高了业绩.
结论:
- ML,特别是随机森林的监督学习,显示出精确的肺声音分析的巨大潜力.
- 这种方法可以提高初步诊断的准确性,改善患者的护理.
- 需要进一步开发以将这些ML解决方案集成到临床实践中.
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