Optimizing Forecasted Activity Notifications with Reinforcement Learning.

In this paper, we propose the notification optimization method by providing multiple alternative times as a reminder for a forecasted activity with and without probabilistic considerations for the activity that needs to be completed and needs notification. It is important to consider various factors when sending notifications to people after obtaining the results of the forecasted activity. We should not send notifications only when we have forecasted results because future daily activities are unpredictable. Therefore, it is important to strike a balance between providing useful reminders and avoiding excessive interruptions, especially for low probabilities of forecasted activity. Our study investigates the impact of the low probability of forecasted activity and optimizes the notification time with reinforcement learning. We also show the gaps between forecasted activities that are useful for self-improvement by people for the balance of important tasks, such as tasks completed as planned and additional tasks to be completed. For evaluation, we utilize two datasets: the existing dataset and data we collected in the field with the technology we have developed. In the data collection, we have 23 activities from six participants. To evaluate the effectiveness of these approaches, we assess the percentage of positive responses, user response rate, and response duration as performance criteria. Our proposed method provides a more effective way to optimize notifications. By incorporating the probability level of activity that needs to be done and needs notification into the state, we achieve a better response rate than the baseline, with the advantage of reaching 27.15%, as well as than the other criteria, which are also improved by using probability.

Estimation of the global prevalence, incidence, years lived with disability of rheumatoid arthritis in 2019 and forecasted incidence in 2040: results from the Global Burden of Disease Study 2019.

OBJECTIVE: We aimed to provide a better understanding of the secular trends in rheumatoid arthritis (RA) burden at the regional and national levels, contributing to identifying the areas with high burden needs and finding the potential areas requiring additional attention, which will facilitate the development of strategies tailored to RA burden. METHOD: Data were obtained from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 study. We presented the secular trends in the prevalence, incidence, and years lived with disability (YLDs) of RA needs by sex, age, sociodemographic index (SDI), region, country, and category between 1990 and 2019 using data from the GBD 2019 study. The age-standardized rates (ASR) and its estimated annual percentage changes (EAPCs) are employed to describe the secular trends in RA. RESULTS: Globally, there were an estimated 18.5 million [95% confidence interval (CI) 31.53 to 41.74)] prevalent cases of RA, with 1.07 million (95% CI 0.95 to 1.18) incident cases per year and almost 2.43 million YLDs (95% CI 1.68 to 3.28) in 2019. The age-standardized prevalence and incidence rates estimated for RA were 224.25 per 100,000 and 12.21 per 100,000 in 2019, with EAPCs of 0.37 (95% CI - 0.32, 0.42) and 0.30 (95% CI 0.25 to 0.34), respectively. The corresponding age-standardized YLDs estimated was 29.35 per 100,000 in 2019, with an EAPC of 0.38 (95% CI: 0.33, 0.43). During the study period, the ASR of RA was consistently higher in females than in males. Moreover, the age-standardized YLD rate of RA was associated with the sociodemographic index (SDI) in 2019 across all 204 countries and territories (R = 0.28). The projections indicate that the age-standardized incidence rates (ASIR) trend will continue to increase from 2019 to 2040, with a projected ASIR of 10.48 and 4.63/100,000 for females and males, respectively. CONCLUSIONS: RA is prevalent and remains a significant global public health challenge. Globally, the burden of RA has increased over the past 30 years and will continue to increase. Prevention and early treatment of RA are pivotal to avoiding disease onset and alleviating the enormous burden. Key Points • The burden of rheumatoid arthritis is increasing globally. • Global estimates indicate that the number of RA incident cases will increase 1.4-fold globally, from approximately 1.07 million at the end of 2019 to approximately 1.5 million by 2040.

Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared.

During the COVID-19 pandemic, excess mortality has been reported worldwide, but its magnitude has varied depending on methodological differences that hinder between-study comparability. Our aim was to estimate variability attributable to different methods, focusing on specific causes of death with different pre-pandemic trends. Monthly mortality figures observed in 2020 in the Veneto Region (Italy) were compared with those forecasted using: (1) 2018-2019 monthly average number of deaths; (2) 2015-2019 monthly average age-standardized mortality rates; (3) Seasonal Autoregressive Integrated Moving Average (SARIMA) models; (4) Generalized Estimating Equations (GEE) models. We analyzed deaths due to all-causes, circulatory diseases, cancer, and neurologic/mental disorders. Excess all-cause mortality estimates in 2020 across the four approaches were: +17.2% (2018-2019 average number of deaths), +9.5% (five-year average age-standardized rates), +15.2% (SARIMA), and +15.7% (GEE). For circulatory diseases (strong pre-pandemic decreasing trend), estimates were +7.1%, -4.4%, +8.4%, and +7.2%, respectively. Cancer mortality showed no relevant variations (ranging from -1.6% to -0.1%), except for the simple comparison of age-standardized mortality rates (-5.5%). The neurologic/mental disorders (with a pre-pandemic growing trend) estimated excess corresponded to +4.0%/+5.1% based on the first two approaches, while no major change could be detected based on the SARIMA and GEE models (-1.3%/+0.3%). The magnitude of excess mortality varied largely based on the methods applied to forecast mortality figures. The comparison with average age-standardized mortality rates in the previous five years diverged from the other approaches due to the lack of control over pre-existing trends. Differences across other methods were more limited, with GEE models probably representing the most versatile option.

Factors influencing the difference between forecasted and actual drug sales volumes under the price-volume agreement in South Korea.

This study analyzed factors contributing to increases in the actual sales volumes relative to forecasted volumes of drugs under price-volume agreement (PVA) policy in South Korea. Sales volumes of newly listed drugs on the national formulary are monitored under PVA policy. When actual sales volume exceeds the pre-agreed forecasted volume by 30% or more, the drug is subject to price-reduction. Logistic regression assessed the factors related to whether drugs were the PVA price-reduction drugs. A generalized linear model with gamma distribution and log-link assessed the factors influencing the increase in actual volumes compared to forecasted volume in the PVA price-reduction drugs. Of 186 PVA monitored drugs, 34.9% were price-reduction drugs. Drugs marketed by pharmaceutical companies with previous-occupation in the therapeutic markets were more likely to be PVA price-reduction drugs than drugs marketed by firms with no previous-occupation. Drugs of multinational pharmaceutical companies were more likely to be PVA price-reduction drugs than those of domestic companies. Having more alternative existing drugs was significantly associated with higher odds of being PVA price-reduction drugs. Among the PVA price-reduction drugs, the increasing rate of actual volume compared to forecasted volume was significantly higher in drugs with clinical usefulness. By focusing the negotiation efforts on those target drugs, PVA policy can be administered more efficiently with the improved predictability of the drug sales volumes.

A Method for Oscillation Errors Restriction of SINS Based on Forecasted Time Series.

Continuity, real-time, and accuracy are the key technical indexes of evaluating comprehensive performance of a strapdown inertial navigation system (SINS). However, Schuler, Foucault, and Earth periodic oscillation errors significantly cut down the real-time accuracy of SINS. A method for oscillation error restriction of SINS based on forecasted time series is proposed by analyzing the characteristics of periodic oscillation errors. The innovative method gains multiple sets of navigation solutions with different phase delays in virtue of the forecasted time series acquired through the measurement data of the inertial measurement unit (IMU). With the help of curve-fitting based on least square method, the forecasted time series is obtained while distinguishing and removing small angular motion interference in the process of initial alignment. Finally, the periodic oscillation errors are restricted on account of the principle of eliminating the periodic oscillation signal with a half-wave delay by mean value. Simulation and test results show that the method has good performance in restricting the Schuler, Foucault, and Earth oscillation errors of SINS.