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机器学习模型的开发和验证,该模型使用语音来预测志向风险.

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机器学习通过分析母音发音,准确地预测吸收风险. 这种新的算法提供了一个非侵入性的工具来评估吞安全性,与专家临床医生相比.

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科学领域:

  • 耳鼻喉科 耳鼻喉科 耳鼻喉科
  • 语音科学 语言科学
  • 人工智能的人工智能

背景情况:

  • 吸气对呼吸道疾病有风险,但目前的诊断方法是侵入性的或不可靠的.
  • 主观的临床评估缺乏一致性,而像VFSS和FEES这样的测试是资源密集的.

研究的目的:

  • 开发和验证用于预测志向风险的机器学习 (ML) 算法.
  • 该算法分析了简单的母音发音的声学特征.

主要方法:

  • 对163名患者的[i]元音发音的回顾性分析,记录声学特征.
  • 被监督的ML模型被训练来区分高风险与低风险吸尘器,使用VFSS进行地面真相.
  • 模型在外部队列上得到验证,并与语音语言病理学家 (SLP) 相比较.

主要成果:

  • ML模型显示,高风险 (0.530) 和低风险 (0.243) 志向组之间的风险得分有显著差异 (p<0.001).
  • 在开发队列中达到0.76的曲线下的面积 (AUC),在外部队列中达到0.70.
  • 在分类吸血风险方面,ML模型的性能与训练有素的SLP相美.

结论:

  • 耳鼻喉科 (ENT) 患者的可量化的语音特征与吸收风险相关.
  • 一个分析持续发音的ML模型可以有效地检测高风险和低风险吸尘器之间的差异.
  • 这种方法提供了一种有希望的,非侵入性的方法,用于志向风险评估.