FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309603721> ?p ?o ?g. }

• W4309603721 abstract "Clinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI).Finding significant features associated with PD-MCI through a machine learning approach.Patients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model.Seventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2.This study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI." @default.
• W4309603721 created "2022-11-28" @default.
• W4309603721 creator A5003687592 @default.
• W4309603721 creator A5005445227 @default.
• W4309603721 creator A5014214000 @default.
• W4309603721 creator A5017841337 @default.
• W4309603721 creator A5018342706 @default.
• W4309603721 creator A5022961448 @default.
• W4309603721 creator A5023390430 @default.
• W4309603721 creator A5044760713 @default.
• W4309603721 creator A5044842089 @default.
• W4309603721 creator A5062438887 @default.
• W4309603721 creator A5070427663 @default.
• W4309603721 creator A5073277956 @default.
• W4309603721 creator A5074115401 @default.
• W4309603721 date "2022-11-17" @default.
• W4309603721 modified "2023-09-26" @default.
• W4309603721 title "Machine learning can predict mild cognitive impairment in Parkinson's disease" @default.
• W4309603721 cites W1605688901 @default.
• W4309603721 cites W1608790292 @default.
• W4309603721 cites W1661600686 @default.
• W4309603721 cites W1942921912 @default.
• W4309603721 cites W1967880316 @default.
• W4309603721 cites W1971404476 @default.
• W4309603721 cites W1972121380 @default.
• W4309603721 cites W1997121325 @default.
• W4309603721 cites W2049223406 @default.
• W4309603721 cites W2053919734 @default.
• W4309603721 cites W2082170771 @default.
• W4309603721 cites W2112455323 @default.
• W4309603721 cites W2116648434 @default.
• W4309603721 cites W2134113727 @default.
• W4309603721 cites W2137310042 @default.
• W4309603721 cites W2139699215 @default.
• W4309603721 cites W2156013560 @default.
• W4309603721 cites W2166298299 @default.
• W4309603721 cites W2286113349 @default.
• W4309603721 cites W2469103211 @default.
• W4309603721 cites W2502518763 @default.
• W4309603721 cites W2556733370 @default.
• W4309603721 cites W2576639105 @default.
• W4309603721 cites W2593272776 @default.
• W4309603721 cites W2599637679 @default.
• W4309603721 cites W2611126732 @default.
• W4309603721 cites W2618847690 @default.
• W4309603721 cites W2619586475 @default.
• W4309603721 cites W2767691921 @default.
• W4309603721 cites W2772779547 @default.
• W4309603721 cites W2788752483 @default.
• W4309603721 cites W2791463426 @default.
• W4309603721 cites W2799984449 @default.
• W4309603721 cites W2884417535 @default.
• W4309603721 cites W2912226467 @default.
• W4309603721 cites W2940810421 @default.
• W4309603721 cites W2944357645 @default.
• W4309603721 cites W2956009551 @default.
• W4309603721 cites W2975008362 @default.
• W4309603721 cites W2991020945 @default.
• W4309603721 cites W3019814059 @default.
• W4309603721 cites W3041343669 @default.
• W4309603721 cites W3047307615 @default.
• W4309603721 cites W3120766175 @default.
• W4309603721 cites W3147407249 @default.
• W4309603721 cites W3174664487 @default.
• W4309603721 cites W4214601296 @default.
• W4309603721 cites W4214689898 @default.
• W4309603721 cites W4283159242 @default.
• W4309603721 doi "https://doi.org/10.3389/fneur.2022.1010147" @default.
• W4309603721 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36468069" @default.
• W4309603721 hasPublicationYear "2022" @default.
• W4309603721 type Work @default.
• W4309603721 citedByCount "1" @default.
• W4309603721 countsByYear W43096037212023 @default.
• W4309603721 crossrefType "journal-article" @default.
• W4309603721 hasAuthorship W4309603721A5003687592 @default.
• W4309603721 hasAuthorship W4309603721A5005445227 @default.
• W4309603721 hasAuthorship W4309603721A5014214000 @default.
• W4309603721 hasAuthorship W4309603721A5017841337 @default.
• W4309603721 hasAuthorship W4309603721A5018342706 @default.
• W4309603721 hasAuthorship W4309603721A5022961448 @default.
• W4309603721 hasAuthorship W4309603721A5023390430 @default.
• W4309603721 hasAuthorship W4309603721A5044760713 @default.
• W4309603721 hasAuthorship W4309603721A5044842089 @default.
• W4309603721 hasAuthorship W4309603721A5062438887 @default.
• W4309603721 hasAuthorship W4309603721A5070427663 @default.
• W4309603721 hasAuthorship W4309603721A5073277956 @default.
• W4309603721 hasAuthorship W4309603721A5074115401 @default.
• W4309603721 hasBestOaLocation W43096037211 @default.
• W4309603721 hasConcept C118552586 @default.
• W4309603721 hasConcept C126322002 @default.
• W4309603721 hasConcept C139719470 @default.
• W4309603721 hasConcept C14216870 @default.
• W4309603721 hasConcept C144301174 @default.
• W4309603721 hasConcept C15744967 @default.
• W4309603721 hasConcept C162324750 @default.
• W4309603721 hasConcept C169900460 @default.
• W4309603721 hasConcept C2776509080 @default.
• W4309603721 hasConcept C2776867660 @default.
• W4309603721 hasConcept C38180746 @default.
• W4309603721 hasConcept C558461103 @default.

• next