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Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Dr. Robert Waldinger didn't believe it at first. The idea that loneliness could break down the body--that the quality of your relationships might help decide whether you get coronary artery disease, arthritis, or Type 2 diabetes--struck the psychiatrist as far-fetched.

Health care company Abbott has entered into a partnership to connect Google Health to its Lingo continuous glucose monitor (CGM). According to the press release, this multi-year collaboration will allow users of the over-the-counter Lingo device to see their metabolic data within the Google Health app, which could help a person "make better lifestyle and nutrition choices in the moment." The Lingo data can also be shared with the Google Health Coach, which offers AI-driven recommendations based on an individual's information. Lingo is explicitly intended for adults who are not using insulin. Engadget reported on the growing trend of non-diabetics using continuous glucose monitors back in 2023 and found little research that supported the benefits for that population to track blood sugar spikes so closely.

Image caption, More than 2,000 images were taken of Ruth's skin I don't mind having my photo taken - triple checked and filtered for Instagram - but 70 cameras pointing at me while I'm down to my knickers is a bit daunting. A robotic voice tells me to stay still and close my eyes as I stand in a huge curved scanner while classical music plays in the background. With a flash of light, 2,000 photographs are taken of my body, in the hope of capturing every mark, freckle and mole to analyse for different skin cancers. This full-body scan is happening at a sci-fi-style clinic in Manchester city centre, with me wearing a dressing gown and hexagon-shaped rubber slippers. The millions of data points collected will create a 3D avatar of my body using AI.

The maker of Wegovy and Ozempic, Novo Nordisk, has launched legal action accusing its arch rival Eli Lilly of false advertising in suggesting its weight-loss drugs perform better. The Danish company filed a lawsuit in the US on Tuesday claiming Eli Lilly, which makes Mounjaro and Zepbound, deployed ad campaigns to create the misleading impression that Eli Lilly's medicines are superior. Novo said its rival compared the highest approved doses of its medicines for obesity and type-2 diabetes with lower doses of Novo Nordisk's, while omitting newer, higher-dose options. The BBC has contacted Eli Lilly for comment. The lawsuit comes as Novo and Eli Lilly are locked in battle to dominate the fast-growing weight-loss drug industry, especially in the US, which analysts have estimated could be worth more than $100bn by 2030.

Can you eat too much sugar free candy? But it'll probably just send you running to the bathroom. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Sugar-free candy generally replaces sugar with either artificial sweeteners or sugar alcohols. Breakthroughs, discoveries, and DIY tips sent six days a week.

Event sequences appear widely in domains such as medicine, finance, and remote sensing, yet modeling them is challenging due to their heterogeneity: sequences often contain multiple event types with diverse structures--for example, electronic health records that mix discrete events like medical procedures with continuous lab measurements. Existing approaches either tokenize all entries, violating natural inductive biases, or ignore parts of the data to enforce a consistent structure. In this work, we propose a simple yet powerful Marked Temporal Point Process (MTPP) framework for modeling event sequences with flexible structure, using a single unified model. Our approach employs a single autoregressive transformer with discrete and continuous prediction heads, capable of modeling variable-length, mixed-type event sequences. The continuous head leverages an expressive normalizing flow to model continuous event attributes, avoiding the numerical integration required for inter-event times in most competing methods.

There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED(Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framework that integrates Bayesian Experimental Design (BED) with large language models (LLMs) to better emulate real-world diagnostic reasoning. At each step, ACTMED selects the test expected to yield the greatest reduction in diagnostic uncertainty for a given patient. LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data. Clinicians can remain in the loop; reviewing test suggestions, interpreting intermediate outputs, and applying clinical judgment throughout. We evaluate ACTMEDon real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use. This represents a step toward transparent, adaptive, and clinician-aligned diagnostic systems that generalize across settings with reduced reliance on domain-specific data.

Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates to treatment that maximizes the empirical welfare defined as the mean of scalar-valued potential outcomes. In this paper, we study offline policy learning with distribution-valued outcomes, where each potential outcome is a probability measure on $\mathbb{R}$ and the reward is defined through a utility functional applied to the Wasserstein barycenter of induced outcome distributions. We establish statistical guarantees for the policy learning framework based on both Inverse Probability Weighting (IPW) and Doubly Robust (DR) estimators. By handling the challenging uniform deviation over the product of the combinatorial policy class and the infinite-dimensional quantile domain, we prove that the finite-sample regret has leading dependence $\widetilde{\mathcal{O}}(\sqrt{\mathrm{N\text{-}dim}(Π)/N})$. In the one-dimensional Wasserstein setting and under the stated regularity conditions, the leading regret rate is still governed by the policy-class complexity. Moreover, we provide a minimax lower bound establishing the sharpness of the leading dependence on $N$ and $\mathrm{N\text{-}dim}(Π)$.

This work presents the first theoretically justified simultaneous inference framework for off-policy evaluation (OPE). In contrast to existing methods that focus on point estimates or pointwise confidence intervals (CIs), the new framework quantifies global uncertainty across an infinite or continuous initial state space, offering valid inference over the entire state space.