The mini mental state examination (MMSE) is a common tool for measuring cognitive decline in Alzhiemer's Disease (AD) subjects. Subjects are usually observed for a specified period of time or until death to determine the trajectory of the decline which for the most part appears to be linear. However, it may be noted that the decline may not be modeled by a single linear model over a specified period of time. There may be a point called a change point where the rate or gradient of the decline may change depending on the length of time of observation. A Bayesian approach is used to model the trajectory and determine an appropriate posterior estimate of the change point as well as the predicted model of decline before and after the change point. Estimates of the appropriate parameters as well as their posterior credible regions or regions of interest are established. Coherent prior to posterior analysis using mainly non informative priors for the parameters of interest is provided. This approach is applied to an existing AD database.
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http://dx.doi.org/10.1016/j.matcom.2009.09.002 | DOI Listing |
Zh Nevrol Psikhiatr Im S S Korsakova
December 2024
Russian University of Medicine, Moscow, Russia.
Objective: Analysis of the effectiveness of the use of the drug Cytoflavin and the organization of the activities of nursing staff, within the framework of nursing care, in the complex therapy of patients with spinal cord injury (PSMT).
Material And Methods: Material and methods. 40 patients with PSMT due to a gunshot wound were examined, who were divided into two equal groups depending on the type of therapy performed: group 1 patients received the full volume of stage I medical rehabilitation (with additional use of neurodevelopmental techniques under the supervision of a Bobata department nurse) and standard drug therapy, including a course of intravenous Cytoflavin infusions followed by tablet form; group 2 patients received the full volume of stage I medical rehabilitation and standard drug therapy, but did not receive Cytoflavin.
BMC Psychiatry
December 2024
Department of Clinical Psychology and Psychotherapy, Institute of Psychology, University of Bern, Fabrikstrasse 8, Bern, 3012, Switzerland.
Background: Healthcare professionals play an important role in successfully implementing digital interventions in routine mental healthcare settings. While a larger body of research has focused on the experiences of mental healthcare professionals with the combination of digital interventions and face-to-face outpatient treatment, comparatively little is known about their experiences with digital interventions combined with inpatient treatment. This is especially true for acute psychiatric inpatient care, where studies on the implementation of digital interventions are more rare.
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December 2024
Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
Alcohol use disorder (AUD) is a chronic relapsing brain disorder characterized by an impaired ability to stop or control alcohol consumption despite adverse social, occupational, or health consequences. AUD affects nearly one-third of adults at some point during their lives, with an associated cost of approximately $249 billion annually in the U.S.
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December 2024
Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi-cho, Kodaira, Tokyo, 187-8553, Japan.
Pupil dilation is considered to track the arousal state linked to a wide range of cognitive processes. A recent article suggested the potential to unify findings in pupillometry studies based on an information theory framework and Bayesian methods. However, Bayesian methods become computationally intractable in many realistic situations.
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December 2024
Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia.
This study investigates the impact of outliers on the evolution of clusters in temporal data-sets. Monitoring and tracing cluster transitions of temporal data sets allow us to observe how clusters evolve and change over time. By tracking the movement of data points between clusters, we can gain insights into the underlying patterns, trends, and dynamics of the data.
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