Idea density for predicting Alzheimer’s disease from transcribed speech

Kairit Sirts, Olivier Piguet, Mark Johnson

Abstract
Idea Density (ID) measures the rate at which ideas or elementary predications are expressed in an utterance or in a text. Lower ID is found to be associated with an increased risk of developing Alzheimer’s disease (AD) (Snowdon et al., 1996; Engelman et al., 2010). ID has been used in two different versions: propositional idea density (PID) counts the expressed ideas and can be applied to any text while semantic idea density (SID) counts pre-defined information content units and is naturally more applicable to normative domains, such as picture description tasks. In this paper, we develop DEPID, a novel dependency-based method for computing PID, and its version DEPID-R that enables to exclude repeating ideas—a feature characteristic to AD speech. We conduct the first comparison of automatically extracted PID and SID in the diagnostic classification task on two different AD datasets covering both closed-topic and free-recall domains. While SID performs better on the normative dataset, adding PID leads to a small but significant improvement (+1.7 F-score). On the free-topic dataset, PID performs better than SID as expected (77.6 vs 72.3 in F-score) but adding the features derived from the word embedding clustering underlying the automatic SID increases the results considerably, leading to an F-score of 84.8.
Anthology ID:
K17-1033
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Roger Levy, Lucia Specia
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
322–332
Language:
URL:
https://aclanthology.org/K17-1033
DOI:
10.18653/v1/K17-1033
Bibkey:
Cite (ACL):
Kairit Sirts, Olivier Piguet, and Mark Johnson. 2017. Idea density for predicting Alzheimer’s disease from transcribed speech. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 322–332, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Idea density for predicting Alzheimer’s disease from transcribed speech (Sirts et al., CoNLL 2017)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/K17-1033.pdf
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