Reading Between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
Braun F, Rüggeberg A, Ranzenberger T, Lehfeld H, Hillemacher T, Bocklet T, Riedhammer K (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 16940 LNCS
Pages Range: 605-616
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Brno, CZE
ISBN: 9783032372482
DOI: 10.1007/978-3-032-37249-9_49
Abstract
Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive symptoms. We compare three LLMs (Mistral 3.1, DeepHermes, Qwen3) in two settings: (1) zero-shot prediction and (2) LLM-based feature extraction for Support Vector Regression, using human and pause-enriched transcripts. Results show that LLMs effectively predict depression severity in zero-shot settings (best MAE of 0.60), while dementia assessment benefits substantially from structured feature extraction (best MAE of 0.78), reducing errors by up to 35% over zero-shot baselines. Pause-enriched transcripts achieve competitive performance with human transcriptions, demonstrating the viability of fully automatic screening pipelines for differential neuropsychiatric assessment.
Involved external institutions
Germany (DE)How to cite
APA:
Braun, F., Rüggeberg, A., Ranzenberger, T., Lehfeld, H., Hillemacher, T., Bocklet, T., & Riedhammer, K. (2027). Reading Between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews. In Elmar Nöth, Aleš Horák, Petr Sojka (Eds.), Lecture Notes in Computer Science (pp. 605-616). Brno, CZE: Springer Science and Business Media Deutschland GmbH.
MLA:
Braun, Franziska, et al. "Reading Between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews." Proceedings of the 29th International Conference on Text, Speech, and Dialogue, TSD 2026, Brno, CZE Ed. Elmar Nöth, Aleš Horák, Petr Sojka, Springer Science and Business Media Deutschland GmbH, 2027. 605-616.
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