Publications by authors named "M SUBERT"
Background And Objectives: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD.
Methods: Spontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing.
Article Synopsis
- Automated analysis of lung CT scans can help classify different subphenotypes of acute respiratory illness in COVID-19 patients by combining CT features with clinical and lab data.
- A study of 559 spontaneously breathing COVID-19 patients identified two subphenotypes: one with older patients, higher inflammation, more severe hypoxemia, and a higher mortality rate, while the other had distinct lung imaging features and lower mortality.
- The findings emphasize the potential of using machine learning and lung-CT imaging to improve the understanding and treatment of respiratory failure in COVID-19 patients.
Article Synopsis
- - This study examined how speech and language impairments relate to outcomes in patients with isolated REM sleep behavior disorder (iRBD) across seven different language-speaking centers.
- - Researchers used advanced speech analysis techniques to identify distinct patterns of language deterioration and followed 180 patients over about 2.7 years, discovering that 26.9% developed neurodegenerative diseases.
- - Results indicated that greater severity in linguistic and acoustic abnormalities significantly predicted the likelihood of developing conditions like dementia or parkinsonism, suggesting automated language analysis can be a useful predictive tool for identifying patients at risk.
Background: Impairment of higher language functions associated with natural spontaneous speech in multiple sclerosis (MS) remains underexplored.
Objectives: We presented a fully automated method for discriminating MS patients from healthy controls based on lexical and syntactic linguistic features.
Methods: We enrolled 120 MS individuals with Expanded Disability Status Scale ranging from 1 to 6.
Background: Patients with synucleinopathies frequently display language abnormalities. However, whether patients with isolated rapid eye movement sleep behavior disorder (iRBD) have prodromal language impairment remains unknown.
Objective: We examined whether the linguistic abnormalities in iRBD can serve as potential biomarkers for conversion to synucleinopathy, including the possible effect of mild cognitive impairment (MCI), speaking task, and automation of analysis procedure.