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https://www.selleckchem.com/products/tetrathiomolybdate.html ch and in clinical practice. Differentiated subtypes of nonadherence according to intentionality seem to exist in patients with schizophrenia and schizoaffective disorder. Our findings suggest the need for differentiated approach, both in future research and in clinical practice. Despite the frequent progression from Parkinson's disease (PD) to Parkinson's disease dementia (PDD), the basis to diagnose early-onset Parkinson dementia (EOPD) in the early stage is still insufficient. To explore the prediction accuracy of sociodemographic factors, Parkinson's motor symptoms, Parkinson's non-motor symptoms, and rapid eye movement sleep disorder for diagnosing EOPD using PD multicenter registry data. This study analyzed 342 Parkinson patients (66 EOPD patients and 276 PD patients with normal cognition), younger than 65 years. An EOPD prediction model was developed using a random forest algorithm and the accuracy of the developed model was compared with the naive Bayesian model and discriminant analysis. The overall accuracy of the random forest was 89.5%, and was higher than that of discriminant analysis (78.3%) and that of the naive Bayesian model (85.8%). In the random forest model, the Korean Mini Mental State Examination (K-MMSE) score, Korean Montreal Cognitive Assessment (K-MoCA), sum of boxes in Clinical Dementia Rating (CDR), global score of CDR, motor score of Untitled Parkinson's Disease Rating (UPDRS), and Korean Instrumental Activities of Daily Living (K-IADL) score were confirmed as the major variables with high weight for EOPD prediction. Among them, the K-MMSE score was the most important factor in the final model. It was found that Parkinson-related motor symptoms ( , motor score of UPDRS) and instrumental daily performance ( , K-IADL score) in addition to cognitive screening indicators ( , K-MMSE score and K-MoCA score) were predictors with high accuracy in EOPD prediction. It was found that Park

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