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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Neuropsychiatric disturbances in mild cognitive impairment: A scientometric analysis
Arisara Amrapala1, Michel Sabé2, Marco Solmi3
1Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Cognitive Fitness and Biopsychiatry Technology Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Center of Excellence in Digital and AI for Mental Health, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Abstract:
Behavioral and psychological symptoms of dementia (BPSD) have been extensively studied in dementia than its prodromal stage, known as mild cognitive impairment (MCI). A scientometric study on BPSD in MCI would be valuable in synthesizing the existing body of research and providing insights into the trends, networks, and influencers within this area. We searched for related literature in the Web of Science database and extracted complete text and citation records of each publication. The primary objective was to map the research evolution of BPSD in MCI and highlight dominant research themes. The secondary objective was to identify research network characteristics (authors, journals, countries, and institutions) and abundances. A total of 12,369 studies published between 1980 and 2022 were included in the analysis. We found 51 distinct clusters from the co-cited reference network that were highly credible with significant modularity (Q = 0.856) and silhouette scores (S = 0.932). Five major research domains were identified: symptoms, diagnosis, brain substrates, biochemical pathways, and interventions. In recent years, the research focus in this area has been on gut microbiota, e-health, COVID-19, cognition, and delirium. Collectively, findings from this scientometric analysis can help clarify the scope and direction of future research and clinical practices.
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