Adverse drug events (ADEs) significantly impact clinical research and public health, contributing to failures in clinical trials and leading to increased healthcare costs. The accurate prediction and management of ADEs are crucial for improving the development of safer, more effective medications, and enhancing patient outcomes. To support this effort, we introduce CT-ADE, a novel dataset compiled to enhance the predictive modeling of ADEs. Encompassing over 12,000 instances extracted from clinical trial results, the CT-ADE dataset integrates drug, patient population, and contextual information for multilabel ADE classification tasks in monopharmacy treatments, providing a comprehensive resource for developing advanced predictive models. To mirror the complex nature of ADEs, annotations are standardized at the system organ class level of the Medical Dictionary for Regulatory Activities (MedDRA) ontology. Preliminary analyses using baseline models have demonstrated promising results, achieving 73.33% F1 score and 81.54% balanced accuracy, highlighting CT-ADE's potential to advance ADE prediction. CT-ADE provides an essential tool for researchers aiming to leverage the power of artificial intelligence and machine learning to enhance patient safety and minimize the impact of ADEs on pharmaceutical research and development. Researchers interested in using the CT-ADE dataset can find all necessary resources at https://github.com/xxxx/xxxx.

The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems (ITS) has contributed to its growth as well as highlighted key challenges. However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective formulation of Markov Decision Process (MDP), can be challenging and often require domain experts in both RL and ITS. Recent advancements in Large Language Models (LLMs) such as GPT-4 highlight their broad general knowledge, reasoning capabilities, and commonsense priors across various domains. In this work, we conduct a large-scale user study involving 70 participants to investigate whether novices can leverage ChatGPT to solve complex mixed traffic control problems. Three environments are tested, including ring road, bottleneck, and intersection. We find ChatGPT has mixed results. For intersection and bottleneck, ChatGPT increases number of successful policies by 150% and 136% compared to solely beginner capabilities, with some of them even outperforming experts. However, ChatGPT does not provide consistent improvements across all scenarios.

In 2018, the New York City Council created a task force to study the city's use of automated decision systems (ADS). The concern: Algorithms, not just in New York but around the country, were increasingly being employed by government agencies to do everything from informing criminal sentencing and detecting unemployment fraud to prioritizing child abuse cases and distributing health benefits. And lawmakers, let alone the people governed by the automated decisions, knew little about how the calculations were being made. Rare glimpses into how these algorithms were performing were not comforting: In several states, algorithms used to determine how much help residents will receive from home health aides have automatically cut benefits for thousands. Police departments across the country use the PredPol software to predict where future crimes will occur, but the program disproportionately sends police to Black and Hispanic neighborhoods.

These are the small group gatherings that are the source of the ongoing surge of COVID-19 infections among students at USC. As the university enters its second week of classes, it's not large shoulder-to-shoulder parties that are the top worry. It's the common, day-to-day interactions among groups of 10 or fewer friends and housemates who live in thousands of privately owned apartments and houses that surround the campus. The situation underscores the challenges other colleges in California face as classes resume this fall, including UCLA, where classes begin Sept. 28. Dr. Sarah Van Orman, chief health officer for USC Student Health, said that since her last update on Monday, 104 new cases of coronavirus have been confirmed -- including the first three on campus -- marking 147 total cases this week.

Evolving technology, best practice and landmark evidence in glaucoma care were reviewed by an international expert faculty in session presentations and debates during the 11th Moorfields International Glaucoma Symposium 2019. The authors were meeting chairs and provide an overview of symposium proceedings. Hans Lemij, Rotterdam Eye Hospital, the Netherlands, discussed glaucoma optical coherence tomography (OCT) imaging and automated segmentation issues, noting several common image artefacts. Paul Foster highlighted research by the UK Biobank Eye and Vision Consortium related to cognitive function and the expanding use of OCT imaging in dementia and neurodegeneration research. Findings show that a thinner retinal nerve fibre layer (RNFL) is associated with worse cognitive function in individuals without known neurodegenerative disease, as well as a greater likelihood of future cognitive decline [1]. The Rotterdam Study also revealed an association of retinal neurodegeneration on OCT with an increased risk of dementia, including Alzheimer's disease [2].

The idea has appeared in my head during a talk given by an Artificial Intelligence expert, Alejandro Saucedo, at my university. It felt really exciting: there was a huge amount of students interested in AI and there was no study group like that at my Uni. What I was waiting for then? That was the beginning of the story of how creating a study group boosted my knowledge about AI and helped me in getting a summer internship in Machine Learning. I have never suspected that it will have such a big impact on me and on students who joined the group.

A new artificial intelligence system that employs Google Glass may be a resource for helping children with autism spectrum disorder (ASD) improve socialization skills, according to a recent study published in JAMA Pediatrics. The small clinical trial found that children using the wearable technology at home showed significant improvements in socialization skills, compared to their counterparts that received only the standard of care. Named Superpower Glass, the new system was designed to "encourage facial engagement" and provide feedback on social situations. The program, which runs on Google Glass, helps kids classify the emotion of the person they are interacting with. Using machine learning, the tools is able to identify eight emotions, and then cue the child via a robotic audio clip and a visual emoticon.

The first Stakeholder Summit on Artificial Intelligence, organised by the European Economic and Social Committee (EESC) and the European Commission, stressed that the EU must ensure that artificial intelligence is safe, unbiased and in line with European values. The event, which aimed to discuss the next steps to advance the EU strategy on artificial intelligence, took place on 18 June in Brussels. The key EU policy-makers on artificial intelligence and European stakeholders who gathered for the first stakeholder summit agreed that artificial intelligence held great promise in terms of addressing societal issues, but also raised a number of challenges with regard to privacy, security, labour, education and ethics. "Artificial intelligence is a technology which does not have to overcome and overwhelm us," said Catelijne Muller, President of the EESC Thematic Study Group on Artificial Intelligence, adding that humans should stay in command of artificial intelligence and be able to determine "if, when and how we want to use these technologies in our daily lives." Artificial intelligence is one of the main political priorities of the EESC's current presidency.