Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don't know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.

This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S. practices integrated with CureMD's EMR/EHR system. Unlike traditional systems--using AI models that rely on features from patients' labs--our approach focuses on routinely available data, such as medical history, vitals, diagnoses, and medications, to preemptively assess the risks of chronic diseases in the next year. We trained three distinct models for each chronic disease: prediction models that forecast the risk of a disease 3, 6, and 12 months before a potential diagnosis. We developed Random Forest models, which were internally validated using F1 scores and AUROC as performance metrics and further evaluated by a panel of expert physicians for clinical relevance based on inferences grounded in medical knowledge. Additionally, we discuss our implementation of integrating these models into a practical EMR system. Beyond using Shapley attributes and surrogate models for explainability, we also introduce a new rule-engineering framework to enhance the intrinsic explainability of Random Forests.

An AI-powered death clock promises to predict the exact day you'll die, right down to the second. The Death Clock app, available for download in Google and Apple stores, analyzes life choices users regularly make, their past habits, health conditions and family history of disease to'accurately' determine when they will die. Users are asked to put in a number of health markers like their cholesterol and blood-sugar levels, as well as their workout schedule, water intake, mental health and the current state of their romantic and plutonic relationships. The app is backed by data from 1,200 international life expectancy studies that looked at 53 million participants, including information from the Centers for Disease Control and Prevention. Although it seemed to be a morbid exercise, I took the test to see exactly how the results would play out.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' As artificial intelligence continues to move into the health care arena -- showing up everywhere from ultrasound screenings to drug development to doctors' offices -- some patients may be uncertain, curious or concerned about how it will impact them or their medical care. Dr. Harvey Castro, an emergency medicine physician in Coppell, Texas, is also a consultant and speaker on AI and ChatGPT in health care. Castro shared with Fox News Digital some of the most common questions patients are asking about the use of AI in the medical field -- and provided his responses. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Artificial intelligence and machine learning are key to unlocking patient data and solving some of healthcare's most complex problems. Even as the U.S. seeks to put the COVID-19 pandemic in the rearview mirror, many who survive the initial illness suffer debilitating long-term health impacts, especially those with underlying health conditions. Technology allows easier access to disparate data sources without compromising data privacy or integrity. In addition, advanced analytics deliver real-time insights, enabling providers to predict outcomes and diagnose illness early to intervene with patients at risk of developing long-term COVID and other chronic diseases. To delve deeper into these technologies and their ramifications in healthcare, Healthcare IT News spoke with Brett Furst, president of HHS Tech Group.

Within precision medicine, and specifically rare diseases, clinicians and researchers rely on genetic and diagnostic testing to help drive accurate diagnosis and treatment. However, genomic data alone are often insufficient to unlock the life-changing diagnoses of rare diseases. Well-curated and accurate phenotype data, which may include quantified observable traits such as short stature, low set ears, and blood biochemistry, along with genetic and diagnostic test results, are vital for shortening the diagnostic journey of these patients and identifying the most effective treatments available. The need for accurate patient phenotyping is not a new concept. In fact, over 20 years ago, Isaac Kohane, Chair of the Department of Biomedical Informatics and the Marion V. Nelson Professor of Biomedical Informatics at Harvard Medical School, predicted that the accurate practice of patient phenotyping would become essential as the volume of genomic information continued to surge.

BOSTON, July 14, 2022 (GLOBE NEWSWIRE) -- juli, the AI-powered digital health platform that delights and engages consumers to power their own health while offering their healthcare providers insights from sub-episodic health data, announced the formation of an advisory board packed with digital health luminaries. Launched more than a year ago, juli helps people manage complex chronic conditions by aggregating and analyzing data from EMRs, smartphones, wearables, the environment, and patient-reported data. By applying AI to these disparate data sources, juli identifies previously unseen correlations and encourages micro-behavioral changes in users that can help alleviate conditions such as depression, bipolar disorder, asthma, migraine, and chronic pain. Joe Kvedar, MD - Professor at Harvard Medical School and digital health pioneer, Kvedar is also Editor in Chief of Nature's npj Digital Medicine and just completed his term as Chair of the Board for the American Telemedicine Association. Kristen Valdes - Founder and CEO of b.well Connected Health, the digital transformation platform, and a former UnitedHealthcare executive, Valdes has over 20 years' experience making healthcare data more easily interoperable with less friction for health plan members and consumers, including as a board member of the CARIN Alliance.

The coronavirus pandemic has tested public health systems globally. Few novel and infectious diseases around the world have ever posed such dramatic challenges as the novel coronavirus SARS-CoV-2, which causes COVID-19. With highly efficient human-to-human transmission and high mortality rates, COVID19 led the World Health Organization to declare a public health emergency of international concern and caused countries around the world to reassess their public health capabilities. The United Arab Emirates, like other members of the international community, faced the unprecedented challenge of ensuring public health and safety while minimizing economic fallout. These efforts by the U.A.E.'s leadership allowed the U.A.E. to be globally ranked as one of the top countries, and the highest in the Arab world, in terms of its COVID-19 response. VPS Healthcare is an integrated healthcare service provider with 22 operational hospitals, over 125 healthcare centres, 13000 employees, one of the largest pharmaceutical manufacturing plants in Dubai and medical support services spread across the Middle East, Europe and India. By providing comprehensive patient management at international quality standards across the MENA Region and beyond and to the entire strata of community, VPS Healthcare reflects a brand image of excellence in healthcare delivery system.

The digital healthcare revolution has already begun, and it will gain further momentum in 2022 as providers and patients look for new and better ways to improve care. Companies with strong offerings, management teams and balance sheets are poised to capture tremendous value. Healthcare deals were hot in the first nine months in 2021. They brought in a total of $21.3 billion in venture funding across 541 deals, dwarfing the previous record of $14.6 billion set in 2020, according to Rock Health. But startups will continue to lead the way in innovation with the use of AI, IoT and data analytics, especially with data becoming the central currency of healthcare.

Zebra Medical Vision's Chief Medical Officer, Dr. Orit Wimpfheimer, on the future of radiology and how to juggle a high-flying career with being a mom of nine Dr. Orit Wimpfheimer is a diagnostic radiologist who founded her Israel-based teleradiology company in 2001. She joined Zebra Medical Vision, initially as clinical director, and now as chief medical officer, bringing her experience to direct and promote AI technology. What initially sparked your interest in medicine and subsequently, AI in medicine? I came from a family of doctors. My father, uncle and two brothers were all doctors, so I grew up in a family where medicine was central to many of our conversations around the dinner table.