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. A quantitative analysis was performed using 6 lexical and syntactic and 2 acoustic features. Results were compared with human-controlled analysis to assess the robustness of the approach. Diagnostic accuracy was evaluated using sensitivity analysis. RESULTS: Despite similar disease duration, linguistic abnormalities were generally more severe in MSA than in PD, leading to high diagnostic accuracy with an area under the curve of 0.81. Compared to controls, MSA showed decreased grammatical component usage, more repetitive phrases, shorter sentences, reduced sentence development, slower articulation rate, and increased duration of pauses, whereas PD had only shorter sentences, reduced sentence development, and longer pauses. Only slower articulation rate was distinctive for MSA while unchanged for PD relative to controls. The highest correlation was found between bulbar/pseudobulbar clinical score and sentence length (r = -0.49, p = 0.002). Despite the relatively high severity of dysarthria in MSA, a strong agreement between manually and automatically computed results was achieved. DISCUSSION: Automated linguistic analysis may offer an objective, cost-effective, and widely applicable biomarker to differentiate synucleinopathies with similar clinical manifestations.

• MeSH
• Diagnosis, Differential MeSH
• Middle Aged MeSH
• Humans MeSH
• Multiple System Atrophy * diagnosis   physiopathology   complications   MeSH
• Parkinson Disease * diagnosis   complications   physiopathology   MeSH
• Speech physiology   MeSH
• Aged MeSH
• Natural Language Processing MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

OBJECTIVE: This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). METHODS: Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story-tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow-up period of 5 years. RESULTS: Of 180 patients free of parkinsonism or dementia, 156 provided follow-up information. After a mean follow-up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia-first (n = 16) and parkinsonism-first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism-first with normal cognition converters (n = 17). INTERPRETATION: Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530-543.

• MeSH
• Dementia * MeSH
• Cognitive Dysfunction * diagnosis   MeSH
• Humans MeSH
• Neurodegenerative Diseases * MeSH
• Parkinsonian Disorders * MeSH
• REM Sleep Behavior Disorder * diagnosis   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH

Verifying the speaker of a speech fragment can be crucial in attributing a crime to a suspect. The question can be addressed given disputed and reference speech material, adopting the recommended and scientifically accepted likelihood ratio framework for reporting evidential strength in court. In forensic practice, usually, auditory and acoustic analyses are performed to carry out such a verification task considering a diversity of features, such as language competence, pronunciation, or other linguistic features. Automated speaker comparison systems can also be used alongside those manual analyses. State-of-the-art automatic speaker comparison systems are based on deep neural networks that take acoustic features as input. Additional information, though, may be obtained from linguistic analysis. In this paper, we aim to answer if, when and how modern acoustic-based systems can be complemented by an authorship technique based on frequent words, within the likelihood ratio framework. We consider three different approaches to derive a combined likelihood ratio: using a support vector machine algorithm, fitting bivariate normal distributions, and passing the score of the acoustic system as additional input to the frequent-word analysis. We apply our method to the forensically relevant dataset FRIDA and the FISHER corpus, and we explore under which conditions fusion is valuable. We evaluate our results in terms of log likelihood ratio cost (Cllr) and equal error rate (EER). We show that fusion can be beneficial, especially in the case of intercepted phone calls with noise in the background.

• MeSH
• Speech Acoustics MeSH
• Algorithms MeSH
• Humans MeSH
• Linguistics MeSH
• Likelihood Functions MeSH
• Speech MeSH
• Forensic Sciences * methods   MeSH
• Support Vector Machine MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

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 6.5 and 120 age-, sex-, and education-matched healthy controls. Linguistic analysis was performed with fully automated methods based on automatic speech recognition and natural language processing techniques using eight lexical and syntactic features acquired from the spontaneous discourse. Fully automated annotations were compared with human annotations. RESULTS: Compared with healthy controls, lexical impairment in MS consisted of an increase in content words (p = 0.037), a decrease in function words (p = 0.007), and overuse of verbs at the expense of noun (p = 0.047), while syntactic impairment manifested as shorter utterance length (p = 0.002), and low number of coordinate clause (p < 0.001). A fully automated language analysis approach enabled discrimination between MS and controls with an area under the curve of 0.70. A significant relationship was detected between shorter utterance length and lower symbol digit modalities test score (r = 0.25, p = 0.008). Strong associations between a majority of automatically and manually computed features were observed (r > 0.88, p < 0.001). CONCLUSION: Automated discourse analysis has the potential to provide an easy-to-implement and low-cost language-based biomarker of cognitive decline in MS for future clinical trials.

• Publication type
• Journal Article MeSH

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. METHODS: We enrolled 139 Czech native participants, including 40 iRBD without MCI and 14 iRBD with MCI, compared with 40 PD without MCI, 15 PD with MCI, and 30 healthy control subjects. Spontaneous discourse and story-tale narrative were transcribed and linguistically annotated. A quantitative analysis was performed computing three linguistic features. Human annotations were compared with fully automated annotations. RESULTS: Compared with control subjects, patients with iRBD showed poorer content density, reflecting the reduction of content words and modifiers. Both PD and iRBD subgroups with MCI manifested less occurrence of unique words and a higher number of n-grams repetitions, indicating poorer lexical richness. The spontaneous discourse task demonstrated language impairment in iRBD without MCI with an area under the curve of 0.72, while the story-tale narrative task better reflected the presence of MCI, discriminating both PD and iRBD subgroups with MCI from control subjects with an area under the curve of up to 0.81. A strong correlation between manually and automatically computed results was achieved. CONCLUSIONS: Linguistic features might provide a reliable automated method for detecting cognitive decline caused by prodromal neurodegeneration in subjects with iRBD, providing critical outcomes for future therapeutic trials. © 2022 International Parkinson and Movement Disorder Society.

• MeSH
• Cognitive Dysfunction * diagnosis   MeSH
• Humans MeSH
• Linguistics MeSH
• Parkinson Disease * complications   MeSH
• REM Sleep Behavior Disorder * diagnosis   MeSH
• Synucleinopathies * MeSH
• Language Development Disorders * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Currently, dyslexia is a frequently discussed topic, which is central in many fields of science, such as special education, speech therapy, psychology, linguistics, or neuroscience. The report is a part of the international research which interconnects all the fields mentioned above. The paper presents a comparison of psychological and special-educational evaluation of adult university students/university alumni with dys-lexia and without it. In this part of the research, 27 participants were assessed by a series of six tests. The data obtained were processed in IBM SPSS Statistics 23 with the help of Multivariate analysis of variance (MANOVA). The results showed that group with dyslexia varied in their test performance aimed at reading and reading abilities, and there were no significant intelligence, attention or short-term memory differences. At the end, the report includes partial data taken from functional magnetic resonance imaging (fMRI).

• MeSH
• Reading MeSH
• Adult MeSH
• Dyslexia * diagnostic imaging   physiopathology   MeSH
• Clinical Studies as Topic MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain diagnostic imaging   MeSH
• Neuropsychological Tests MeSH
• Statistics as Topic MeSH
• Students MeSH
• Universities MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Czech Republic MeSH

Zdravotnická dokumentace má v České republice obvykle povahu volného textu formátovaného jen pomocí mezer, tabulátorů a nových řádků. Extrakce informací z takových zpráv představuje výzvu, jejíž splnění by umožnilo její snadné využívání při poskytování zdravotní péče v zahraničí či převod do strukturované formy. Pro identifikaci a extrakci informací lze využít různých přístupů, od regulární analýzy po pokročilou lingvistickou analýzu a statistické metody. Článek shrnuje naše poznatky získané při regulární analýze a při využití běžně využívaných klasifikačních systémů.

Healthcare documentation in the Czech Republic usually has the form of a free text formatted just using spaces, tabs and line breaks. Extracting information from such a documentation is a challenge that if fulfilled would allow to use Czech medical reports by physicians with no knowledge of the Czech language as well as information transfer to a structured form. It is possible to approach this task as a task of finite-state machine, as a task of the linguistic analysis or as a task of statistics. This article summarizes our findings gained using finite-state machines and using commonly used code lists. Excerpts from real medical reports are translated to English in a way that demonstrates the same or similar problems as in the Czech language. Original Czech excerpts are available in the Czech version of this article.

• Keywords
• zdravotnická dokumentace, elektronický zdravotní záznam, regulární analýza, lingvistická analýza,
• MeSH
• Medical Records Systems, Computerized standards   utilization   MeSH
• Financing, Organized MeSH
• Clinical Laboratory Techniques standards   MeSH
• Humans MeSH
• Linguistics methods   standards   MeSH
• Medical Subject Headings utilization   MeSH
• International Classification of Diseases utilization   MeSH
• Reference Standards MeSH
• Terminology as Topic MeSH
• Information Storage and Retrieval methods   MeSH
• Abbreviations as Topic MeSH
• Natural Language Processing MeSH
• Check Tag
• Humans MeSH

• MeSH
• Medical Informatics MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• lékařská informatika