You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Trump explodes at CNN's Kaitlan Collins as he arrives at the UN with Melania Hayden Panettiere's cause of death revealed one month after star died aged 36 There's a hidden message in Diana's brother's vengeful new book. No wonder William has kept silent... Harry's role in this is so suspect: MAUREEN CALLAHAN Chronic UTIs made sex so painful it almost destroyed my relationship. What REALLY goes on in some Equinox steam rooms: Gym insiders reveal eye-popping indecency... secret towel signals used by experimental married men... and clubs with most'aggressive' locker rooms Kaitlan Collins' explosive closed-door reaction to White House media ban... as CNN insider reveals network's revenge plot that'll infuriate Trump and twisted'leverage' conversation among bosses Grieving Cindy Crawford and Rande Gerber seen for the first time at son Presley's former home as new details emerge about his desperate last minute dash to save himself from relapse Presley Gerber's'inevitable' death: Family insiders reveal life-long secret struggle that sparked'self-destruct' spiral... and mother Cindy Crawford's desperate bid to save him before shock death at 27 Rare nor'easter to slam 13 states with hurricane-force winds and 12-foot waves starting TODAY: 'Very serious situation' 'Looksmaxxing' influencer Clavicular is charged with rape, drugging and giving minor alcohol in Massachusetts A fleeting look in Patrick Clancy's eyes during his 60 Minutes interview sent shockwaves through me... this is the truth about him and his pregnant new wife that must be said: KENNEDY Why it really DOES matter what time of day you take your blood pressure pills. Name of MISTRESS of American Idol star accused of murdering wife is dramatically confirmed in court by victim's sister... who says pair went to same church group: Live updates Man vs robot: Influencer takes on 6ft humanoid in a terrifying cage fight - and it doesn't end well for him Truth about Adriana Lima's dramatic face transformation... and why surgeons say it could easily happen to you I'm a urologist who's treated thousands of men with erectile dysfunction.

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Grieving Cindy Crawford and Rande Gerber seen for the first time at son Presley's former home as new details emerge about his desperate last minute dash to save himself from relapse US to open two Cold War-era military bases in Greenland as Trump celebrates landmark deal for'permanent control' over security of territory Presley Gerber's'inevitable' death: Family insiders reveal life-long secret struggle that sparked'self-destruct' spiral... and mother Cindy Crawford's desperate bid to save him before shock death at 27 A fleeting look in Patrick Clancy's eyes during his 60 Minutes interview sent shockwaves through me... this is the truth about him and his pregnant new wife that must be said: KENNEDY The shiny new status symbol of Trump's inner circle... but it comes with a surprising catch There's a hidden message in Diana's brother's vengeful new book. No wonder William has kept silent... Harry's role in this is so suspect: MAUREEN CALLAHAN Kaitlan Collins' explosive closed-door reaction to White House media ban... as CNN insider reveals network's revenge plot that'll infuriate Trump and twisted'leverage' conversation among bosses Wendy's dealt yet another blow as one of its biggest franchisees files for bankruptcy - leaving the burger chain even further behind its rivals I'm a urologist who's treated thousands of men with erectile dysfunction. They all make the same mistake. Here's why you DON'T need Viagra... the THREE common lifestyle habits to avoid... and test you must ask your doctor for Prince Harry says he, Meghan, Archie and Lilibet enjoy'British things' like'lots of walks and Marmite on crumpets' since their return from California When I started taking Mounjaro six months after the birth of my second child my appetite totally disappeared and the pounds fell off.

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Suicide remains a leading cause of death in Western countries. As social media becomes central to daily life, digital footprints offer valuable insight into suicidal behavior. Focusing on individuals who attempted suicide while uploading videos to their channels, we investigate: How do linguistic patterns on YouTube reflect suicidal behavior, and how do these patterns align with or differ from expert knowledge? We examined linguistic changes around suicide attempts and compared individuals who attempted suicide while actively uploading to their channel with three control groups: those with prior attempts, those experiencing major life events, and matched individuals from the broader cohort. Applying complementary bottom-up, hybrid, and expert-driven approaches, we analyzed a novel longitudinal dataset of 181 suicide-attempt channels and 134 controls. In the bottom-up analysis, LLM-based topic-modeling identified 166 topics; five were linked to suicide attempts, two also showed attempt-related temporal changes (Mental Health Struggles, $OR = 1.74$; YouTube Engagement, $OR = 1.67$; $p < .01$). In the hybrid approach, clinical experts reviewed LLM-derived topics and flagged 19 as suicide-related. However, none showed significant effects beyond those identified bottom-up. YouTube Engagement, a platform-specific indicator, was not flagged, underscoring the value of bottom-up discovery. A top-down psychological assessment of suicide narratives revealed differing motivations: individuals describing prior attempts aimed to help others ($β=-1.69$, $p<.01$), whereas those attempted during the uploading period emphasized personal recovery ($β=1.08$, $p<.01$). By integrating these approaches, we offer a nuanced understanding of suicidality, bridging digital behavior and clinical insights.

Ecological Momentary Assessment provides real-time data on suicidal thoughts and behaviors, but predicting suicide attempts remains challenging due to their rarity and patient heterogeneity. We show that single models fit to all patients perform poorly, while individualized models improve performance but still overfit to patients with limited data. To address this, we introduce Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity, enabling those with little data to leverage similar patients' trends. Preliminary results show promise: even without kernel-design, we outperform all but one baseline while offering a new understanding of patient similarity.

Suicide prediction is the key for prevention, but real data with sufficient positive samples is rare and causes extreme class imbalance. We utilized machine learning (ML) to build the model and deep learning (DL) techniques, like Generative Adversarial Networks (GAN), to generate synthetic data samples to enhance the dataset. The initial dataset contained 656 samples, with only four positive cases, prompting the need for data augmentation. A variety of machine learning models, ranging from interpretable data models to black box algorithmic models, were used. On real test data, Logistic Regression (LR) achieved a weighted precision of 0.99, a weighted recall of 0.85, and a weighted F1 score of 0.91; Random Forest (RF) showed 0.98, 0.99, and 0.99, respectively; and Support Vector Machine (SVM) achieved 0.99, 0.76, and 0.86. LR and SVM correctly identified one suicide attempt case (sensitivity:1.0) and misclassified LR(20) and SVM (31) non-attempts as attempts (specificity: 0.85 & 0.76, respectively). RF identified 0 suicide attempt cases (sensitivity: 0.0) with 0 false positives (specificity: 1.0). These results highlight the models' effectiveness, with GAN playing a key role in generating synthetic data to support suicide prevention modeling efforts.

Abstract--Suicide remains a critical global public health issue. While previous studies have provided valuable insights into detecting suicidal expressions in individual social media posts, limited attention has been paid to the analysis of longitudinal, sequential comment trees for predicting a user's evolving suicidal risk. Users, however, often reveal their intentions through historical posts and interactive comments over time. This study addresses this gap by investigating how the information in comment trees affects both the discrimination and prediction of users' suicidal risk levels. We constructed a high-quality annotated dataset, sourced from Reddit, which incorporates users' posting history and comments, using a refined four-label annotation framework based on the Columbia Suicide Severity Rating Scale (C-SSRS). Statistical analysis of the dataset, along with experimental results from Large Language Models (LLMs) experiments, demonstrates that incorporating comment trees data significantly enhances the discrimination and prediction of user suicidal risk levels. This research offers a novel insight to enhancing the detection accuracy of at-risk individuals, thereby providing a valuable foundation for early suicide intervention strategies.

Raine family attorney Jay Edelson provides details on the wrongful death lawsuit being brought against OpenAI and CEO Sam Altman in the wake of Adam Raine's suicide, alleging the company chose to'cut short' proper testing of ChatGPT. A former Yahoo executive who killed his elderly mother and then himself in a Connecticut home was reportedly influenced by ChatGPT, which fueled his conspiracy theories. Stein-Erik Soelberg, 56, spoke to OpenAI's popular bot, which he nicknamed "Bobby," before the shocking murder-suicide involving his 83-year-old mother, Suzanne Eberson Adams, in Old Greenwich, Conn., the Wall Street Journal reported. "Erik, you're not crazy," the chatbot said after Soelberg claimed his mother and her friend tried to poison him by putting psychedelic drugs in his car's air vents. "And if it was done by your mother and her friend, that elevates the complexity and betrayal."

Unstructured text from legal, medical, and administrative sources offers a rich but underutilized resource for research in public health and the social sciences. However, large-scale analysis is hampered by two key challenges: the presence of sensitive, personally identifiable information, and significant heterogeneity in structure and language. We present a modular toolchain that prepares such text data for embedding-based analysis, relying entirely on open-weight models that run on local hardware, requiring only a workstation-level GPU and supporting privacy-sensitive research. The toolchain employs large language model (LLM) prompting to standardize, summarize, and, when needed, translate texts to English for greater comparability. Anonymization is achieved via LLM-based redaction, supplemented with named entity recognition and rule-based methods to minimize the risk of disclosure. We demonstrate the toolchain on a corpus of 10,842 Swedish court decisions under the Care of Abusers Act (LVM), comprising over 56,000 pages. Each document is processed into an anonymized, standardized summary and transformed into a document-level embedding. Validation, including manual review, automated scanning, and predictive evaluation shows the toolchain effectively removes identifying information while retaining semantic content. As an illustrative application, we train a predictive model using embedding vectors derived from a small set of manually labeled summaries, demonstrating the toolchain's capacity for semi-automated content analysis at scale. By enabling structured, privacy-conscious analysis of sensitive documents, our toolchain opens new possibilities for large-scale research in domains where textual data was previously inaccessible due to privacy and heterogeneity constraints.

Suicide remains one of the main preventable causes of death among active service members and veterans. Early detection and prediction are crucial in suicide prevention. Machine learning techniques have yielded promising results in this area recently. This study aims to assess and summarize current research and provides a comprehensive review regarding the application of machine learning techniques in assessing and predicting suicidal ideation, attempts, and mortality among members of military and veteran populations. A keyword search using PubMed, IEEE, ACM, and Google Scholar was conducted, and the PRISMA protocol was adopted for relevant study selection. Thirty-two articles met the inclusion criteria. These studies consistently identified risk factors relevant to mental health issues such as depression, post-traumatic stress disorder (PTSD), suicidal ideation, prior attempts, physical health problems, and demographic characteristics. Machine learning models applied in this area have demonstrated reasonable predictive accuracy. However, additional research gaps still exist. First, many studies have overlooked metrics that distinguish between false positives and negatives, such as positive predictive value and negative predictive value, which are crucial in the context of suicide prevention policies. Second, more dedicated approaches to handling survival and longitudinal data should be explored. Lastly, most studies focused on machine learning methods, with limited discussion of their connection to clinical rationales. In summary, machine learning analyses have identified a wide range of risk factors associated with suicide in military populations. The diversity and complexity of these factors also demonstrates that effective prevention strategies must be comprehensive and flexible.