Learning diagnostic models using speech and language measures.
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.
Peintner
Bart
B
SRI International, Menlo Park, CA, USA. peintner@ai.sri.com
Jarrold
William
W
Vergyriy
Dimitra
D
Richey
Colleen
C
Tempini
Maria Luisa Gorno
ML
Ogar
Jennifer
J
D001185:Artificial Intelligence; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D001330:Electronic Data Processing; D005625:Frontal Lobe / Q000473:pathology; D006801:Humans; D008037:Linguistics; D019636:Neurodegenerative Diseases / Q000175:diagnosis / Q000503:physiopathology; D009483:Neuropsychological Tests; D011597:Psychomotor Performance; D015203:Reproducibility of Results; D013016:Sound; D013060:Speech; D014706:Verbal Learning