title: Learning diagnostic models using speech and language measures.
abstract: 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.
authors.affiliation: SRI International, Menlo Park, CA, USA. peintner@ai.sri.com
authors.forename: Bart
authors.initials: B
authors.lastname: Peintner
authors.forename: William
authors.initials: W
authors.lastname: Jarrold
authors.forename: Dimitra
authors.initials: D
authors.lastname: Vergyriy
authors.forename: Colleen
authors.initials: C
authors.lastname: Richey
authors.forename: Maria Luisa Gorno
authors.initials: ML
authors.lastname: Tempini
authors.forename: Jennifer
authors.initials: J
authors.lastname: Ogar
mesh_terms: 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