Mild cognitive impairment understanding: an empirical study by data-driven approach

Abstract

BackgroundCognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy.ResultsIn this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined.ConclusionBy incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.

The effectiveness of non‐pharmacological interventions targeting neuropsychiatric symptoms among persons with preclinical and mild dementia: A systematic review and network meta‐analysis

“…The model postulates that NPS are a product of unmet needs due to the increasing difficulties in expressing ones' needs 82 . Considering that the daily functioning of people at earlier disease stages is mostly intact, 83 their NPS manifestations are more stress‐provoking as described by the PLST model than need‐driven as stated by the unmet need model 22 …”

“…The model postulates that NPS are a product of unmet needs due to the increasing difficulties in expressing ones' needs. 82 Considering that the daily functioning of people at earlier disease stages is mostly intact, 83 their NPS manifestations are more stress-provoking as described by the PLST model than need-driven as stated by the unmet need model. 22 Using systematic observation, we found that cognitive, psychobehavioral and educative components need to be designed to optimize patients' functional performance, help them develop a positive outlook on themselves and the ability to control negative emotions, and increase their cognitive orientation to their illness.…”

Palm Fruit Bioactive Complex (PFBc), a Source of Polyphenols, Demonstrates Potential Benefits for Inflammaging and Related Cognitive Function

“…A recent study analyzed data obtained from the 2016 Centers for Disease Control and Prevention (CDC) based on Behavioral Risk Factor Surveillance System (BRFSS) nationwide telephone survey. They reported that the factors contributing to MCI include depression, physical health, cigarette usage, education level, sleep time, kidney disease, alcohol consumption, and exercise [ 15 ]. It has been identified that there are several reversible risk factors for MCI that should be focused on as addressing these factors may help to delay or offset the progression of MCI to dementia.…”

“…These risk factors include family history, the presence of vascular risk factors such as hypertension, hyperlipidemia, coronary artery disease, and stroke [ 12 ], and having the apolipoprotein E allele [ 13 ]. Age is the strongest risk factor, with a diet low in fruits and vegetables and polyphenols and lack of physical activity (lifestyle) also recognized as strong contributing factors [ 14 , 15 ]. A diet rich in fruits and vegetables has been shown to lower the risk of chronic diseases including age-related cognitive decline and the risk of developing neurodegenerative disease [ 16 , 17 , 18 ].…”

Efficient publication of distributed and overlapping graph data under differential privacy

“…Among these contents, graph data have constituted an imperative component because of their capability to capture both semantic and structural information [2] . Typical instances of graph data include social interactions [3,4] , bioinformatic contents [5,6] , semantic webs [7] , road Xu Zheng, Lizong Zhang, and Kaiyang Li are with the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. E-mail: fxzheng, lzhangg@uestc.edu.cn; kaiyang.li@ outlook.com.…”