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Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital.

Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital.

Large Language Models (LLMs) are transforming mental health care by enhancing accessibility, personalization, and efficiency in therapeutic interventions. These AI-driven tools empower mental health professionals with real-time support, improved data integration, and the ability to encourage care-seeking behaviors, particularly in underserved communities. By harnessing LLMs, practitioners can deliver more empathetic, tailored, and effective support, addressing longstanding gaps in mental health service provision. However, their implementation comes with significant challenges and ethical concerns. Performance limitations, data privacy risks, biased outputs, and the potential for generating misleading information underscore the critical need for stringent ethical guidelines and robust evaluation mechanisms. The sensitive nature of mental health data further necessitates meticulous safeguards to protect patient rights and ensure equitable access to AI-driven care. Proponents argue that LLMs have the potential to democratize mental health resources, while critics warn of risks such as misuse and the diminishment of human connection in therapy. Achieving a balance between innovation and ethical responsibility is imperative. This paper examines the transformative potential of LLMs in mental health care, highlights the associated technical and ethical complexities, and advocates for a collaborative, multidisciplinary approach to ensure these advancements align with the goal of providing compassionate, equitable, and effective mental health support.

Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating the bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital.

We all love to have a cuddle with our furry friends, and now a new study has shed light on exactly why that is. Researchers at the University of Basel in Switzerland compared brain scans of study participants while they were stroking a pooch and a cuddly toy. They found that viewing, feeling, and touching the dog engaged the part of the brain that regulates and processes social or emotional interactions - known as the prefrontal cortex - in a way that petting the cuddly toy didn't. It is hoped their findings will improve treatments in animal-assisted clinical therapy for patients who struggle with motivation and attention. 'Prefrontal brain activity in healthy subjects increased with a rise in interactional closeness with a dog or a plush animal, but especially in contact with the dog the activation is stronger,' the authors concluded.

While the term "healthcare consumerism" has been used since the 1930s, today the term refers to the importance of creating a more patient or consumer-centered experience. Patients want a more integrated, seamless healthcare experience that focuses on their particular needs. Artificial intelligence (A.I.), big tech, and big data give patients more transparency, more choice, and more flexibility across the healthcare ecosystem, which helps to facilitate a more positive healthcare experience. But the use of A.I. and machine learning to improve the patient experience, particularly and most importantly in treatment outcomes, begins long before the application of telemedicine, online appointment setting, digitalization, access to real-time information and price transparency, all of which are being used within the ecosystem with varying degrees of success. Where does healthcare consumerism really begin?

In collaboration with Oregon Health & Science University and Weill Cornell Medicine, the goal is to code an easy-to-use smart phone app to help assess whether a follow-up medical diagnosis is needed. "Alzheimer's is tough to deal with and it's very easy to confuse its early stage, mild cognitive impairment, with normal cognitive decline as we're getting older," said Zhou, who leads a research group in the Department of Computer Science and Engineering. Although this AI approach might sound like science fiction, Zhou and his team have already shown in preliminary tests that it is as accurate as MRIs in recognizing early warning signs. These tests used data collected by collaborators at Oregon Health & Science University who are leading a clinical trial studying how conversations might serve as therapeutic intervention for dementia or early Alzheimer's. Joining Zhou on this grant are Hiroko Dodge, a professor of neurology at Oregon Health & Science University, and Fei Wang, an assistant professor of health care policy and research at Weill Cornell Medicine.

Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital.

IMAGE: Using age predictors within specified age groups to infer causality and identify therapeutic interventions. The deep age predictors can help advance aging research by establishing causal relationships in nonlinear systems. Deep aging clocks can be used for identification of novel therapeutic targets, evaluating the efficacy of various interventions, data quality control, data economics, prediction of health trajectories, mortality, and many other applications. Dr. Alex Zhavoronkov from Insilico Medicine, Hong Kong Science and Technology Park, in Hong Kong, China & The Buck Institute for Research on Aging in Novato, California, USA as well as The Biogerontology Research Foundation in London, UK said "The recent hype cycle in artificial intelligence (AI) resulted in substantial investment in machine learning and increase in available talent in almost every industry and country." Over many generations humans have evolved to develop from a single-cell embryo within a female organism, come out, grow with the help of other humans, reach reproductive age, reproduce, take care of the young, and gradually decline.