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ARXIV:2602.11028 · HEALTHCARE AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.11028HEALTHCARE AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts.
Opportunity summary
Pain Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
Healthcare AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts.
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10.48550/arXiv.2602.11028Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts.
Abstract
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches. Methods: This study analyzes spontaneous speech transcripts from the DementiaBank Pitt Corpus using three linguistic representations: raw cleaned text, a part-of-speech (POS)-enhanced representation combining lexical and grammatical information, and a POS-only syntactic representation. Logistic regression and random forest models were evaluated under two protocols: transcript-level train-test splits and subject-level five-fold cross-validation to prevent speaker overlap. Model interpretability was examined using global feature importance, and statistical validation was conducted using Mann-Whitney U tests with Cliff's delta effect sizes. Results: Across representations, models achieved stable performance, with syntactic and grammatical features retaining strong discriminative power even in the absence of lexical content. Subject-level evaluation yielded more conservative but consistent results, particularly for POS-enhanced and POS-only representations. Statistical analysis revealed significant group differences in functional word usage, lexical diversity, sentence structure, and discourse coherence, aligning closely with machine learning feature importance findings. Conclusion: The results demonstrate that abstract linguistic features capture robust markers of early cognitive decline under clinically realistic evaluation. By combining interpretable machine learning with non-parametric statistical validation, this study supports the use of linguistically grounded features for transparent and reliable language-based cognitive screening.
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Dimensions overall score 7.0
PROBLEM
Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
METHOD
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
WHY NOW
Healthcare AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Background: Subtle changes in spontaneous language production are among the earliest indicators of cognitive decline. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Identifying linguistically interpretable markers of dementia can support transparent and clinically grounded screening approaches.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Healthcare AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop an AI tool to detect early cognitive decline using linguistic markers from speech transcripts.
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