ArticleNPJ Parkinson's disease2022
Detecting Parkinson's disease and its cognitive phenotypes via automated semantic analyses of action stories.
Article in NPJ Parkinson's disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Extending standardized speech analysis across languages: An Italian toolkit for transcription and coding of neurological speech.Behavior research methods · 2026Article
- Article
- The include network: Advancing cross-linguistic equity in brain health research.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study.The Clinical neuropsychologist · 2026Article
- Spontaneous speech enables scalable digital phenotyping of physical functional deficits in aging.npj aging · 2026Article
- Speech and language outcome measures in clinical trials of Alzheimer's and Parkinson's diseases.Expert review of neurotherapeutics · 2026Review
- Cognitive Phenotyping of Parkinson's Disease Patients Via Digital Analysis of Spoken Word Properties.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability.Journal of medical Internet research · 2025Article
- Digital phenotyping of Parkinson's disease via natural language processing.NPJ Parkinson's disease · 2025Article
- Linguistic changes in spontaneous speech for detecting Parkinson's disease using large language models.PLOS digital health · 2025Article
- Motor symptoms of Parkinson's disease: critical markers for early AI-assisted diagnosis.Frontiers in aging neuroscience · 2025Review
- Dynamics of language grounding: on the time course, durability, adaptability, and vulnerability of embodied effects.Frontiers in psychology · 2025Review
- Advancements in dementia research, diagnostics, and care in Latin America: Highlights from the 2023 Alzheimer's Association International conference satellite symposium in Mexico City.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- Electrophysiological alterations during action semantic processing in Parkinson's disease.Neurobiology of aging · 2024Article
- Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson's Disease through Artificial Intelligence: A Systematic Review.Brain sciences · 2024Review
- Neurocognitive correlates of semantic memory navigation in Parkinson's disease.NPJ Parkinson's disease · 2024Article
- Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia.PloS one · 2024Article
- Speech and language markers of neurodegeneration: a call for global equity.Brain : a journal of neurology · 2023Review
- Narrative discourse production in Parkinson's disease: Decoupling the role of cognitive-linguistic and motor speech changes.Heliyon · 2023Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 6 countries.
Funding
Abstract
Action-concept outcomes are useful targets to identify Parkinson's disease (PD) patients and differentiate between those with and without mild cognitive impairment (PD-MCI, PD-nMCI). Yet, most approaches employ burdensome examiner-dependent tasks, limiting their utility. We introduce a framework capturing action-concept markers automatically in natural speech. Patients from both subgroups and controls retold an action-laden and a non-action-laden text (AT, nAT). In each retelling, we weighed action and non-action concepts through our automated Proximity-to-Reference-Semantic-Field (P-RSF) metric, for analysis via ANCOVAs (controlling for cognitive dysfunction) and support vector machines. Patients were differentiated from controls based on AT (but not nAT) P-RSF scores. The same occurred in PD-nMCI patients. Conversely, PD-MCI patients exhibited reduced P-RSF scores for both texts. Direct discrimination between patient subgroups was not systematic, but it yielded best outcomes via AT scores. Our approach outperformed classifiers based on corpus-derived embeddings. This framework opens scalable avenues to support PD diagnosis and phenotyping.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.