Intervention to promote preventive dental care for older Korean-American Medi-Cal enrollees in Los Angeles.

BACKGROUND: In California, preventive dental care is covered by Medi-Cal (California's Medicaid program). However, many beneficiaries do not use their dental benefits. Given that a lack of knowledge about oral health and insurance coverage contributes to this underutilization, promoting the use of dental benefits among eligible individuals via an educational program is imperative. Responding to the particular needs of older immigrants with limited English proficiency, we developed a digital oral health intervention for older Korean-American Medi-Cal enrollees in Los Angeles. This educational intervention is designed to be delivered via computers and the Internet. It consists of a 15-min self-running PowerPoint presentation narrated in Korean with links to additional information on the Internet. The slides contain information about the basic etiology of oral diseases, oral hygiene, common myths about oral health and dental care, Medi-Cal coverage of preventive dental care, and how to find a dental clinic. METHODS: We pilot tested the intervention with 12 participants to examine its feasibility and acceptability. We also obtained participants' qualitative feedback about the intervention. RESULTS: A post-intervention quantitative assessment yielded high participant satisfaction and improved oral health and dental care knowledge. Participant responses to the intervention yielded four themes: (1) content and structure, (2) linguistic and cultural aspects, (3) delivery mode, and (4) additional concerns and suggestions. CONCLUSIONS: Our findings confirm the intervention's feasibility and acceptability and suggest further refinement.

Predicting the Progression of Mild Cognitive Impairment to Alzheimer's Dementia Using Recurrent Neural Networks With a Series of Neuropsychological Tests.

BACKGROUND AND PURPOSE: The prevalence of Alzheimer's dementia (AD) is increasing as populations age, causing immense suffering for patients, families, and communities. Unfortunately, no treatments for this neurodegenerative disease have been established. Predicting AD is therefore becoming more important, because early diagnosis is the best way to prevent its onset and delay its progression. METHODS: Mild cognitive impairment (MCI) is the stage between normal cognition and AD, with large variations in its progression. The disease can be effectively managed by accurately predicting the probability of MCI progressing to AD over several years. In this study we used the Alzheimer's Disease Neuroimaging Initiative dataset to predict the progression of MCI to AD over a 3-year period from baseline. We developed and compared various recurrent neural network (RNN) models to determine the predictive effectiveness of four neuropsychological (NP) tests and magnetic resonance imaging (MRI) data at baseline. RESULTS: The experimental results confirmed that the Preclinical Alzheimer's Cognitive Composite score was the most effective of the four NP tests, and that the prediction performance of the NP tests improved over time. Moreover, the gated recurrent unit model exhibited the best performance among the prediction models, with an average area under the receiver operating characteristic curve of 0.916. CONCLUSIONS: Timely prediction of progression from MCI to AD can be achieved using a series of NP test results and an RNN, both with and without using the baseline MRI data.

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms.

Background and Objective: Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods: We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results: Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions: Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.