Background Since the onset of the COVID-19 pandemic, there has been a global effort to develop vaccines that protect against COVID-19. Individuals who are fully vaccinated are far less likely to contract and therefore transmit the virus to others. Researchers have found that the internet and social media both play a role in shaping personal choices about vaccinations. Objective This study aims to determine whether supplementing COVID-19 vaccine uptake forecast models with the attitudes found in tweets improves over baseline models that only use historical vaccination data. Methods Daily COVID-19 vaccination data at the county level was collected for the January 2021 to May 2021 study period. Twitter’s streaming application programming interface was used to collect COVID-19 vaccine tweets during this same period. Several autoregressive integrated moving average models were executed to predict the vaccine uptake rate using only historical data (baseline autoregressive integrated moving average) and individual Twitter-derived features (autoregressive integrated moving average exogenous variable model). Results In this study, we found that supplementing baseline forecast models with both historical vaccination data and COVID-19 vaccine attitudes found in tweets reduced root mean square error by as much as 83%. Conclusions Developing a predictive tool for vaccination uptake in the United States will empower public health researchers and decisionmakers to design targeted vaccination campaigns in hopes of achieving the vaccination threshold required for the United States to reach widespread population protection.
BACKGROUND Patient engagement is a critical but challenging public health priority in behavioral healthcare. During telehealth sessions, healthcare providers need to rely more on verbal strategies than typical non-verbal cues to engage patients. Hence, the typical patient engagement behaviors are now different, and provider training on telehealth patient engagement is unavailable or quite limited. Therefore, we explore the application of machine learning for estimating patient engagement to assist psychotherapists in better diagnosis of mental disorders during telemental health sessions. OBJECTIVE The objective of this study was to examine the ability of machine learning models to estimate patient engagement levels during a telemental health session and understand whether the machine learning approach could support mental disorder diagnosis by psychotherapists. METHODS We propose a multimodal learning-based framework MET. We uniquely leverage latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature to understand a person’s level of engagement. Given the labeled data constraints that exist in healthcare, we explore a semi-supervised solution using GANs. To further the development of similar technologies that can be useful for telehealth, we also plan to release a dataset MEDICA containing 1299 video clips, each 3 seconds long and show experiments on the same. The efficacy of our method is also demonstrated through real-world experiments. RESULTS Our framework reports a 40% improvement in RMSE (Root Mean Squared Error) over state-of-the-art methods for engagement estimation. In our real-world tests, we also observed positive correlations between the working alliance inventory scores reported by psychotherapists. This indicates the potential of the proposed model to present patient engagement estimations that aligns well with the engagement measures used by psychotherapists. CONCLUSIONS The performance of the framework described here has been compared against other existing engagement detection machine learning models. We also validated the model using a limited sample of real-world data. Patient engagement in literature has been identified to be important to improve therapeutic alliance. But little research has been undertaken to measure it in a telehealth setting wherein the conventional cues are not available to the therapist to take a confident decision. The framework developed is an attempt to model person-oriented engagement modeling theories within machine learning frameworks to estimate the level of engagement of the patient accurately and reliably in telehealth. The results are encouraging and emphasize the value of combining psychology and machine learning to understand patient engagement. Further testing in actual telehealth settings is necessary to fully assess its usefulness in helping therapists gauge patient engagement during virtual sessions. However, the proposed approach and the creation of the new dataset, MEDICA, opens avenues for future research and development of impactful tools for telehealth.
Place-based short-term crime prediction models leverage the spatio-temporal patterns of historical crimes to predict aggregate volumes of crime incidents at specific locations over time. Under the umbrella of the crime opportunity theory, that suggests that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based short-term crime models. Researchers have used call detail records (CDR), data from location-based services such as Foursquare or from social media to characterize human mobility; and have shown that mobility metrics, together with historical crime data, can improve short-term crime prediction accuracy. In this paper, we propose to use a publicly available fine-grained human mobility dataset from a location intelligence company to explore the effects of human mobility features on short-term crime prediction. For that purpose, we conduct a comprehensive evaluation across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we show that adding human mobility flow features to historical crimes can improve the F1 scores for a variety of neural crime prediction models across cities and types of crimes, with improvements ranging from 2% to 7%. Our analysis also shows that some neural architectures can slightly improve the crime prediction performance when compared to non-neural regression models by at most 2%.
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