Peintner Bart, Jarrold William, Vergyriy Dimitra, Richey Colleen, Tempini Maria Luisa Gorno, Ogar Jennifer
SRI International, Menlo Park, CA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:4648-51. doi: 10.1109/IEMBS.2008.4650249.
We describe results that show the effectiveness of machine learning in the automatic diagnosis of certain neurodegenerative diseases, several of which alter speech and language production. We analyzed audio from 9 control subjects and 30 patients diagnosed with one of three subtypes of Frontotemporal Lobar Degeneration. From this data, we extracted features of the audio signal and the words the patient used, which were obtained using our automated transcription technologies. We then automatically learned models that predict the diagnosis of the patient using these features. Our results show that learned models over these features predict diagnosis with accuracy significantly better than random. Future studies using higher quality recordings will likely improve these results.
我们描述的结果表明，机器学习在某些神经退行性疾病的自动诊断中具有有效性，其中几种疾病会改变言语和语言表达。我们分析了9名对照受试者和30名被诊断患有额颞叶痴呆三种亚型之一的患者的音频。从这些数据中，我们提取了音频信号的特征以及患者使用的词汇，这些词汇是通过我们的自动转录技术获得的。然后，我们使用这些特征自动学习预测患者诊断结果的模型。我们的结果表明，基于这些特征学习的模型预测诊断的准确率显著高于随机猜测。未来使用更高质量录音的研究可能会改善这些结果。
