S: Background: The aim of this study was to evaluate the most effective combination of autoregressive integrated moving average (ARIMA), a time series model, and association rule mining (ARM) techniques to identify meaningful prognostic factors and predict the number of cases for efficient COVID-19 crisis management. Methods: The 3685 COVID-19 patients admitted at Thailand’s first university field hospital following the four waves of infections from March 2020 to August 2021 were analyzed using the autoregressive integrated moving average (ARIMA), its derivative to exogenous variables (ARIMAX), and association rule mining (ARM). Results: The ARIMA (2, 2, 2) model with an optimized parameter set predicted the number of the COVID-19 cases admitted at the hospital with acceptable error scores (R² = 0.5695, RMSE = 29.7605, MAE = 27.5102). Key features from ARM (symptoms, age, and underlying diseases) were selected to build an ARIMAX (1, 1, 1) model, which yielded better performance in predicting the number of admitted cases (R² = 0.5695, RMSE = 27.7508, MAE = 23.4642). The association analysis revealed that hospital stays of more than 14 days were related to the healthcare worker patients and the patients presented with underlying diseases. The worsening cases that required referral to the hospital ward were associated with the patients admitted with symptoms, pregnancy, metabolic syndrome, and age greater than 65 years old. Conclusions: This study demonstrated that the ARIMAX model has the potential to predict the number of COVID-19 cases by incorporating the most associated prognostic factors identified by ARM technique to the ARIMA model, which could be used for preparation and optimal management of hospital resources during pandemics.

Background: Falls are the leading cause of injury among older adults, with vision impairment recognized as a significant risk factor. However, many existing studies have been limited by small sample sizes, retrospective designs, or insufficient adjustment for confounding factors. To overcome these limitations, we used data from the University of Michigan’s Health and Retirement Study (HRS) to analyze the association between self-reported vision and fall risk among older adults in a large, nationally representative sample. Objective: The objective of this study was to investigate the association between vision impairment and falls and assess whether subjective vision impairment predicts future falls in older adults. Methods: This cross-sectional and longitudinal analysis used data from the HRS (1996‐2020) to assess the relationship between self-reported vision, glaucoma history, and falls among US adults aged 65 years and older. HRS uses a biennial, multistage area probability sample survey design, collecting data with community-dwelling individuals followed up every 2 years until death, tracking health, economic, and social outcomes. Multivariate logistic regression was used to analyze associations between self-reported vision and self-reported falls in the past 2 years. Results: A total of 38,835 respondents contributed 117,834 observations. The weighted proportion of participants reporting falls was 37.9% (95% CI 37.7%‐40.1%). Significant risk factors for falls included overall eyesight impairment (adjusted odds ratio [aOR] 1.36, 95% CI 1.20‐1.56), distance vision impairment (aOR 1.37, 95% CI 1.32‐1.42), near vision impairment (aOR 1.33, 95% CI 1.27‐1.37), and glaucoma (aOR 1.15, 95% CI 1.07‐1.24). A similar association was observed for serious falls, where overall eyesight impairment (aOR 1.20, 95% CI 1.03‐1.44), distance vision impairment (aOR 1.14, 95% CI 1.07‐1.22), near vision impairment (aOR 1.12, 95% CI 1.05‐1.18), and glaucoma (aOR 1.15, 95% CI 1.05‐1.26) were significant. In longitudinal analyses, overall vision impairment (aOR 1.23, 95% CI 1.16‐1.29), distance vision impairment (aOR 1.27, 95% CI 1.20‐1.38), near vision impairment (aOR 1.23, 95% CI 1.19‐1.32), and glaucoma (aOR 1.25, 95% CI 1.13‐1.37) increased the risk of future falls. Reported overall vision was significantly associated with the number of falls in both the same (P<.001) and subsequent (P<.001) survey cycles. Conclusions: Both distance and near vision impairment, as well as glaucoma, are associated with a higher risk of falls in older adults and present possible areas for intervention and prevention.

Objective A 5-year survival rate is a predictor for the assessment of oral cancer prognosis. The purpose of this study is to analyze oral cancer data to discover and rank the prognostic factors associated with oral cancer 5-year survival using the association rule mining (ARM) technique. Materials and Methods This study is a retrospective analysis of 897 oral cancer patients from a regional cancer center between 2011 and 2017. The 5-year survival rate was assessed. The multivariable Cox proportional hazards analysis was performed to determine prognostic factors. ARM was applied to clinicopathologic and treatment modalities data to identify and rank the prognostic factors associated with oral cancer 5-year survival. Results The 5-year overall survival rate was 35.1%. Multivariable Cox proportional hazards analysis showed that tumor (T) stage, lymph node metastasis, surgical margin, extranodal extension, recurrence, and distant metastasis of tumor were significantly associated with overall survival rate (p < 0.05). The top associated death within 5 years rule was positive extranodal extension, followed by positive perineural and lymphovascular invasion, with confidence levels of 0.808, 0.808, and 0.804, respectively. Conclusion This study has shown that extranodal extension, and perineural and lymphovascular invasion were the top ranking and major deadly prognostic factors affecting the 5-year survival of oral cancer.

The rapid growth and unique advantages of cryptocurrencies have made them an attractive asset for investment portfolios. However, investing in cryptocurrencies comes with risks, especially market volatility. This paper explores the benefits of Bitcoin’s market dominance, including liquidity, stability, and practical utility. It also reviews multiple previous articles about using Artificial Intelligence (AI) for cryptocurrency trading to enhance the feasibility and profitability of cryptocurrency investments, focusing specifically on Bitcoin. It further discusses the potential advantages of using AI in developing predictive models for market trends and managing grid trading strategies to stabilize returns and lower risks. While research on the application of grid trading strategy with AI in the cryptocurrency market is limited, existing studies in other asset classes demonstrate the effectiveness of AI-based methods. Finally, this paper outlines novel potential future research that aims to integrate AI with the Grid Trading Strategy. This integration seeks to enhance the sustainability of cryptocurrency investments while simultaneously amplifying their profitability.