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
Cyril Varghese1, Jianwei Zhang2, Sara Charney3
1Division of Pulmonary and Department of Critical Care Medicine, Mayo Clinic in Arizona, 5777 East Mayo Blvd, Phoenix, US.
JMIR formative research
|February 4, 2026
概括
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.
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.
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