You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Hayden Panettiere's ex Wladimir Klitschko speaks out after star's death and vows to'always speak of her with respect' to their daughter Kaya, 11 Cocaine kingpins at one of America's biggest party schools made frat pledges package drugs as part of their initiation, police say Husband's double life is exposed as he's arrested for art teacher wife's murder after her gashed body was found in their bloodstained marital home Jared Kushner admits Iran peace deal talks are in dire straits and warns Trump will have to be'very patient' Hayden Panettiere's'second mom' reveals behind-the-scenes heartache: 'She never stood a chance' Lakers' record-breaking $12.5 BILLION sale descends into chaos as Jeanie Buss claims family plan to sell stake is'VOID' in stunning legal complaint Perez Hilton's heartbreaking question from his hospital bed after knife horror and the devastating truth his family ...

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Hayden Panettiere reported as suffering'cardiac arrest' and'overdose' on 911 call before being pronounced dead Trump threatens to'BOMB the s*** out of' US ally in unhinged tirade as ceasefire runs out Devastating truth about Hayden Panettiere's rift with her mother that was'worse than anyone knows': Her icy comments about their relationship just months before daughter's death All the disturbing revelations in Hayden Panettiere's tell-all memoir: Domestic violence, family rift and how she was'put to bed' with a British rockstar The Lindsay Clancy murder trial has horrified America. It's time for someone to say what no one else will about her husband Patrick: KENNEDY Taylor Swift's BABY plans revealed: New details of her exact timeline with Travis... and'pact' with Selena Gomez that's a brutal'slap in the face' for Blake Lively Hayden Panettiere's final hours: Actress flew from LA ...

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A California mother says her teenage son used a chatbot for drug guidance over months before dying of an overdose, raising concerns about AI safety and safeguards.

Can Tech Get Rid of Bad Trips? In this episode of, we talk about some of the latest drug trends and all the ways drugs are changing as they continue to be intertwined with tech. Whether it's teenagers reviving the Benadryl TikTok challenge or people signing up for an out-of-body experience program previously used by the CIA, some of us are chasing unconventional trips--bad trips, essentially. But these trends are happening at a time when AI companies are also looking to create a "cleaner" trip for users, and others are using AI chatbots to therapeutically guide their psychedelic trips. Host Michael Calore sits down with staff writer Boone Ashworth and senior editor Manisha Krishnan to discuss these trends--and the promises and limitations of relying on tech to avoid bad trips. Young People Are Tripping on Benadryl--and It's Always a Bad Time The CIA Used This Psychic Meditation Program. It's Never Been More Popular Please help us improve by filling out our listener survey . Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Hey, Mike, how are you? This is your first appearance on, is it not? It's really nice to be back in the studio.

-- The ability to predict drug overdose risk from a patient's medical records is crucial for timely intervention and prevention. Traditional machine learning models have shown promise in analyzing longitudinal medical records for this task. However, recent advancements in large language models (LLMs) offer an opportunity to enhance prediction performance by leveraging their ability to process long textual data and their inherent prior knowledge across diverse tasks. In this study, we assess the effectiveness of Open AI's GPT -4o LLM in predicting drug overdose events using patients' longitudinal insurance claims records. We evaluate its performance in both fine-tuned and zero-shot settings, comparing them to strong traditional machine learning methods as baselines. Our results show that LLMs not only outperform traditional models in certain settings but can also predict overdose risk in a zero-shot setting without task-specific training. Drug overdose (OD) is a major public health crisis in the United States, leading to a substantial number of emergency medical interventions and fatalities each year. According to the Centers for Disease Control and Prevention (CDC), drug overdoses claimed approximately 107,941 [1] lives in the U.S. in 2022, highlighting the urgent need for effective prevention and intervention strategies. Besides fatal outcomes and lost quality of life for patients, the misuse of prescription medications, illicit drugs, and polysubstance abuse has placed an immense burden on healthcare systems, emergency responders, and policymakers. Identifying individuals at risk early can facilitate timely interventions, such as targeted clinical assessments, behavioral support, and prescription monitoring, thereby reducing the likelihood of fatal outcomes. Md Sultan Al Nahian is with the Institute for Biomedical Informatics, University of Kentucky, Lexington, KY 40536 USA. Chris Delcher and Daniel Harris are with the Department of Pharmacy Practice and Science, University of Kentucky, Lexington, KY 40536 USA. Peter Akpunonu is with the Department of Emergency Medicine, University of Kentucky, Lexington, KY 40536 USA.

The opioid overdose epidemic remains a critical public health crisis, particularly in the United States, leading to significant mortality and societal costs. Social media platforms like Reddit provide vast amounts of unstructured data that offer insights into public perceptions, discussions, and experiences related to opioid use. This study leverages Natural Language Processing (NLP), specifically Opioid Named Entity Recognition (ONER-2025), to extract actionable information from these platforms. Our research makes four key contributions. First, we created a unique, manually annotated dataset sourced from Reddit, where users share self-reported experiences of opioid use via different administration routes. This dataset contains 331,285 tokens and includes eight major opioid entity categories. Second, we detail our annotation process and guidelines while discussing the challenges of labeling the ONER-2025 dataset. Third, we analyze key linguistic challenges, including slang, ambiguity, fragmented sentences, and emotionally charged language, in opioid discussions. Fourth, we propose a real-time monitoring system to process streaming data from social media, healthcare records, and emergency services to identify overdose events. Using 5-fold cross-validation in 11 experiments, our system integrates machine learning, deep learning, and transformer-based language models with advanced contextual embeddings to enhance understanding. Our transformer-based models (bert-base-NER and roberta-base) achieved 97% accuracy and F1-score, outperforming baselines by 10.23% (RF=0.88).

This paper introduces JiraiBench, the first bilingual benchmark for evaluating large language models' effectiveness in detecting self-destructive content across Chinese and Japanese social media communities. Focusing on the transnational "Jirai" (landmine) online subculture that encompasses multiple forms of self-destructive behaviors including drug overdose, eating disorders, and self-harm, we present a comprehensive evaluation framework incorporating both linguistic and cultural dimensions. Our dataset comprises 10,419 Chinese posts and 5,000 Japanese posts with multidimensional annotation along three behavioral categories, achieving substantial inter-annotator agreement. Experimental evaluations across four state-of-the-art models reveal significant performance variations based on instructional language, with Japanese prompts unexpectedly outperforming Chinese prompts when processing Chinese content. This emergent cross-cultural transfer suggests that cultural proximity can sometimes outweigh linguistic similarity in detection tasks. Cross-lingual transfer experiments with fine-tuned models further demonstrate the potential for knowledge transfer between these language systems without explicit target language training. These findings highlight the need for culturally-informed approaches to multilingual content moderation and provide empirical evidence for the importance of cultural context in developing more effective detection systems for vulnerable online communities.

Optimal allocation of scarce resources is a common problem for decision makers faced with choosing a limited number of locations for intervention. Spatiotemporal prediction models could make such decisions data-driven. A recent performance metric called fraction of best possible reach (BPR) measures the impact of using a model's recommended size K subset of sites compared to the best possible top-K in hindsight. We tackle two open problems related to BPR. First, we explore how to rank all sites numerically given a probabilistic model that predicts event counts jointly across sites. Ranking via the per-site mean is suboptimal for BPR. Instead, we offer a better ranking for BPR backed by decision theory. Second, we explore how to train a probabilistic model's parameters to maximize BPR. Discrete selection of K sites implies all-zero parameter gradients which prevent standard gradient training. We overcome this barrier via advances in perturbed optimizers. We further suggest a training objective that combines likelihood with a decision-aware BPR constraint to deliver high-quality top-K rankings as well as good forecasts for all sites. We demonstrate our approach on two where-to-intervene applications: mitigating opioid-related fatal overdoses for public health and monitoring endangered wildlife.