Missouri is testing medical drones to move blood and lab samples faster as rural patients face longer travel times for care.

Background: User reviews of Telehealth and Patient Portal mobile applications (apps) hereon referred to as electronic health (eHealth) apps are a rich source of unsolicited patient feedback, revealing critical insights into patient perceptions. However, the lack of large-scale, annotated datasets specific to privacy and trust has limited the ability of researchers to systematically analyze these concerns using natural language processing (NLP) techniques. Objective: This study aims to develop and benchmark Health App Reviews for Privacy & Trust (HARPT), a large-scale annotated corpus of patient reviews from eHealth apps to advance research in patient privacy and trust. Methods: We employed a multistage data construction strategy. This integrated keyword-based filtering, iterative manual labeling with review, targeted data augmentation, and weak supervision using transformer-based classifiers. A curated subset of 7,000 reviews was manually annotated to support machine learning model development and evaluation. The resulting dataset was used to benchmark a broad range of models. Results: The HARPT corpus comprises 480,000 patient reviews annotated across seven categories capturing critical aspects of trust in the application (TA), trust in the provider (TP), and privacy concerns (PC). We provide comprehensive benchmark performance for a range of machine learning models on the manually annotated subset, establishing a baseline for future research. Conclusions: The HARPT corpus is a significant resource for advancing the study of privacy and trust in the eHealth domain. By providing a large-scale, annotated dataset and initial benchmarks, this work supports reproducible research in usable privacy and trust within health informatics. HARPT is released under an open resource license.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Two winters ago, more than a year after my old college roommate and dear friend Paul was diagnosed with ALS, he started making pictures. By then, he was gradually losing the ability to do almost everything else. He could still walk at that point, often through the leafy corner of his Boston neighborhood, Jamaica Plain, where the old tree limbs cradled the houses and the streets were barely wide enough for a car, but only with the help of a cane. A condition of the disease called bulbar palsy slowed his tongue to the point his words wobbled enough that he sounded as if he were drunk. He could eat solid foods, albeit with some trouble, and could drink the Relyvrio medication powder he swirled with a spoon into a glass of water twice daily--a prescription for ALS that last year clinical trials suggested was ineffective, and a cocktail so bitter it made him physically wince--but he began coughing more and more as he labored to swallow anything at all.

Legal intake, the process of finding out if an applicant is eligible for help from a free legal aid program, takes significant time and resources. In part this is because eligibility criteria are nuanced, open-textured, and require frequent revision as grants start and end. In this paper, we investigate the use of large language models (LLMs) to reduce this burden. We describe a digital intake platform that combines logical rules with LLMs to offer eligibility recommendations, and we evaluate the ability of 8 different LLMs to perform this task. We find promising results for this approach to help close the access to justice gap, with the best model reaching an F1 score of .82, while minimizing false negatives.

Generative AI, such as OpenAI's GPT-4V large-language model, has rapidly entered mainstream discourse. Novel capabilities in image processing and natural-language communication may augment existing forecasting methods. Large language models further display potential to better communicate weather hazards in a style honed for diverse communities and different languages. This study evaluates GPT-4V's ability to interpret meteorological charts and communicate weather hazards appropriately to the user, despite challenges of hallucinations, where generative AI delivers coherent, confident, but incorrect responses. We assess GPT-4V's competence via its web interface ChatGPT in two tasks: (1) generating a severe-weather outlook from weather-chart analysis and conducting self-evaluation, revealing an outlook that corresponds well with a Storm Prediction Center human-issued forecast; and (2) producing hazard summaries in Spanish and English from weather charts. Responses in Spanish, however, resemble direct (not idiomatic) translations from English to Spanish, yielding poorly translated summaries that lose critical idiomatic precision required for optimal communication. Our findings advocate for cautious integration of tools like GPT-4V in meteorology, underscoring the necessity of human oversight and development of trustworthy, explainable AI.

Today it is trivially easy to play games on a computer, games console or phone with your friends over the internet. But before the wide availability of high-speed internet, things were more complicated. In the 1990s and early 2000s, 3D graphics in video games were becoming more and more complex, but the low network speeds of the period meant that these games, unlike slower-paced and less graphically intensive strategy games, were nearly unplayable over an internet connection. In this moment, in which communications technology was being outpaced by graphical power, the Lan (local area network) party was born. The term itself conjures up strong sensory memories for those who were there – sweaty bodies packed into a basement or convention hall, a dozen hefty computer monitors being manoeuvred into position. For those on the outside, these were scenes of incomprehension or ridicule.

The importance of monitoring and predicting droughts is underscored by their frequent occurrence in diverse geographical landscapes (Ghozat et al., 2023). Moreover, the likelihood of droughts is expected to increase in the context of global climate change (Xiujia et al., 2022). Their accurate forecasting, however, is a complex problem due to the inherent difficulty in predicting the onset, duration, and cessation of drought events (Mishra and Desai, 2005). This complexity necessitates the development of sophisticated forecasting models that can effectively navigate these challenges. To frame our problem, it is essential to define the prediction target and establish a suitable time horizon for forecasting (Zhang et al., 2019). Given our focus on long-term decision-making, we aim to generate forecasts that extend 12 months into the future. Selecting an appropriate target for drought prediction is more challenging due to its dependence on multiple climatic factors, including temperature and precipitation. Among the various drought severity indices, the Standardized Precipitation Index (SPI) (McKee et al., 1993) and the Palmer Drought Severity Index (PDSI) (Alley, 1984) stand out as fundamental measures.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Each morning when she wakes up, Juanita Mengel removes the silicone liner of her prosthetic leg out from under a heated blanket so that the metal parts of the artificial limb don't feel as cold on her skin when she straps the pieces together. The 67-year-old Amanda, Ohio, resident then does the same for her 5-year-old dilute tortoiseshell cat, Lola-Pearl, who is missing her left hind leg. The duo is one of an estimated 200 therapy cat teams registered in the U.S. through Pet Partners.

Over the last decade, there has been an effort by lawmakers to reduce incarceration in the United States without impacting public safety. This effort includes parole boards making risk-based parole decisions -- releasing people assessed to be at low risk of committing a crime after being released. To determine how effective the current system of risk-based parole is, researchers from the UC Davis Violence Prevention Research Program and the University of Missouri, Kansas City, used machine learning to analyze parole data from New York. They suggest the New York State Parole Board could safely grant parole to more inmates. The study, "An Algorithmic Assessment of Parole Decisions," was published in the Journal of Quantitative Criminology.

The exceptional ability of ChatGPT to engage in human-like conversations and generate outputs ranging from code to music has caused it to gain widespread popularity as an online phenomenon in recent months.However, despite the extensive discussions and attention the chatbot has received, people know relatively little about the individual who created it – Sam Altman, who is also a co-founder of OpenAI.Thus, even as ChatGPT continues to capture the public's imagination, the question of who Sam Altman is remains unanswered. Sam Altman, who is currently 37 years old, was born in 1985 in Chicago, Illinois. He spent his childhood in St. Louis, Missouri. When he was just eight years old, he got gift as Macintosh computer. He quickly learned to program and disassemble due to his precociousness and efficiency.