Cognitive Digital Biomarkers from Automated Transcription of Spoken Language.

Autor: Tavabi N; Rhoda Au, 72 E. Concord Street, Boston University School of Medicine, Boston, MA 02118. Telephone: (617) 358-0089; email: rhodaau@bu.edu., Stück D, Signorini A, Karjadi C, Al Hanai T, Sandoval M, Lemke C, Glass J, Hardy S, Lavallee M, Wasserman B, Ang TFA, Nowak CM, Kainkaryam R, Foschini L, Au R
Jazyk: angličtina
Zdroj: The journal of prevention of Alzheimer's disease [J Prev Alzheimers Dis] 2022; Vol. 9 (4), pp. 791-800.
DOI: 10.14283/jpad.2022.66
Abstrakt: Background: Although patients with Alzheimer's disease and other cognitive-related neurodegenerative disorders may benefit from early detection, development of a reliable diagnostic test has remained elusive. The penetration of digital voice-recording technologies and multiple cognitive processes deployed when constructing spoken responses might offer an opportunity to predict cognitive status.
Objective: To determine whether cognitive status might be predicted from voice recordings of neuropsychological testing.
Design: Comparison of acoustic and (para)linguistic variables from low-quality automated transcriptions of neuropsychological testing (n = 200) versus variables from high-quality manual transcriptions (n = 127). We trained a logistic regression classifier to predict cognitive status, which was tested against actual diagnoses.
Setting: Observational cohort study.
Participants: 146 participants in the Framingham Heart Study.
Measurements: Acoustic and either paralinguistic variables (e.g., speaking time) from automated transcriptions or linguistic variables (e.g., phrase complexity) from manual transcriptions.
Results: Models based on demographic features alone were not robust (area under the receiver-operator characteristic curve [AUROC] 0.60). Addition of clinical and standard acoustic features boosted the AUROC to 0.81. Additional inclusion of transcription-related features yielded an AUROC of 0.90.
Conclusions: The use of voice-based digital biomarkers derived from automated processing methods, combined with standard patient screening, might constitute a scalable way to enable early detection of dementia.
Competing Interests: Drs. Stück, Signorini, Kainkaryam, and Foschini and Ms. Lemke and Ms. Sandoval are employees of Evidation Health, Inc., a company whose mission is to measure health in everyday life using technology. Dr. Au is a scientific advisor to Signant Health and Biogen; none of the current work described is related to her advisory roles. All other authors declare no conflict of interest.
Databáze: MEDLINE
Externí odkaz: