TY - JOUR
T1 - Automatic subtyping of individuals with primary progressive aphasia
AU - Themistocleous, Charalambos
AU - Ficek, Bronte
AU - Webster, Kimberly
AU - den Ouden, Dirk Bart
AU - Hillis, Argye E.
AU - Tsapkini, Kyrana
N1 - Funding Information:
We thank our funding resources for their support: the Science of Learning Institute grant ‘Effects of tDCS in PPA’ from Johns Hopkins University to KT, and NIH/NIDCD R01 DC14475 to KT. We thank Ms. Olivia Hermann for her assistance in this work.
Funding Information:
We thank our funding resources for their support: the Science of Learning Institute grant 'Effects of tDCS in PPA' from Johns Hopkins University to KT, and NIH/NIDCD R01 DC14475 to KT. We thank Ms. Olivia Hermann for her assistance in this work.
Publisher Copyright:
© 2021 - IOS Press. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Background: The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. Objective: The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA. Methods: In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians' classifications. Results: The DNN model outperformed the other machine learning models as well as expert clinicians' classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants. Conclusion: We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.
AB - Background: The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. Objective: The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA. Methods: In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians' classifications. Results: The DNN model outperformed the other machine learning models as well as expert clinicians' classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants. Conclusion: We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.
KW - Classification
KW - Machine learning
KW - Natural language processing
KW - Primary progressive aphasia
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U2 - 10.3233/JAD-201101
DO - 10.3233/JAD-201101
M3 - Article
C2 - 33427742
AN - SCOPUS:85100580168
VL - 79
SP - 1185
EP - 1194
JO - Journal of Alzheimer's Disease
JF - Journal of Alzheimer's Disease
SN - 1387-2877
IS - 3
ER -