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AI-powered RPM can help address the rural neonatal care crisis

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As hospital consolidation continues nationwide, rural areas are beginning to take a new shape – and it is not a pretty picture. According to a recent study from Health Affairs, newly acquired rural hospitals are eliminating surgical care services and mental health treatment access, despite a sharp rise in depression, suicide and addiction in the hard-hit rural communities. Even more stunning, these newly acquired hospitals are more likely to eliminate maternity and neonatal care than those that remain independent. Coupled with a worsening nursing shortage, this is a huge problem for rural American families. Even before the pandemic, maternal and infant outcomes in the U.S. were shockingly poor.


Artificial intelligence identifies individuals at risk for heart disease complications

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For the first time, University of Utah Health scientists have shown that artificial intelligence could lead to better ways to predict the onset and course of cardiovascular disease. The researchers, working in conjunction with physicians from Intermountain Primary Children's Hospital, developed unique computational tools to precisely measure the synergistic effects of existing medical conditions on the heart and blood vessels. The researchers say this comprehensive approach could help physicians foresee, prevent, or treat serious heart problems, perhaps even before a patient is aware of the underlying condition. Although the study only focused on cardiovascular disease, the researchers believe it could have far broader implications. In fact, they suggest that these findings could eventually lead to a new era of personalized, preventive medicine.


Seeing into the future: Personalized cancer screening with artificial intelligence

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While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist regarding when and how often they should be administered. On the one hand, advocates argue for the ability to save lives: Women aged 60-69 who receive mammograms, for example, have a 33 percent lower risk of dying compared to those who don't get mammograms. Meanwhile, others argue about costly and potentially traumatic false positives: A meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography. Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch-all: Women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind.


Machine learning process can predict which Covid patients will recover from the disease - TechiAI

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Researchers may have developed a new tool that uses machine learning to better predict health outcomes for hospitalized Covid patients, and help physicians make more informed treatment decisions. A German research team from Charity-University Medicine in Berlin – one of the country's largest university hospitals – developed an Artificial Intelligence tool that can estimate how well an infected person will fare based off of a blood sample. The levels of fourteen proteins found in a person's blood can indicate whether a person who suffers a severe enough hospitalization will survive or die from the virus, and the tool developed by researchers can accurately asses their risk. In times of crisis, where resources are especially scarce, the tool can help determine what patients require the most intensive care to survive, and who is more fit to fight off the virus themselves. Using blood samples from Covid patients, a German research team has found that levels of 14 proteins can help determine whether a person survives the virus.


Retinal age gap as a predictive biomarker for mortality risk

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Aim To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk. Methods A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. Results The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. Conclusions Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions. Data are available in a public, open access repository.


Privacy-aware Early Detection of COVID-19 through Adversarial Training

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Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the literature to detect coronavirus using routine clinical data (blood tests, and vital signs). Data breaches and information leakage when using these models can bring reputational damage and cause legal issues for hospitals. In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied research area. In this work, we examine two machine learning approaches, intended to predict a patient's COVID-19 status using routinely collected and readily available clinical data.


The Myriad Applications Of Ambient Intelligence In Healthcare

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Ambient intelligence is a futuristic concept that makes explicit input and output data collection devices redundant in smart cities. Instead, data capturing and processing tools such as sensors, processors and actuators are embedded in everyday objects encountered by smart city inhabitants. Ambient intelligence, a concept related to pervasive computing, will exist in smart cities to add an added layer of functionality and convenience by adapting to user needs constantly. The embedded sensors and processors will be configured to collect contextual data from users, while AI-based tools will be deployed to draw inferences from the information collected to anticipate their future needs. Ambient intelligence makes pervasive computing more human-centric, a trait that is essential for healthcare.


Tales of Finnagus Boggs, Confessions of a Marid Djinn

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All of the names, characters, places, and events portrayed in this novel are either products of the author's imagination or are used fictitiously. Any resemblance to actual persons, living or dead, events, or locales is entirely coincidental. No part of this publication may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical photocopying, recording, or otherwise, without written permission of the publisher. Information regarding permission, write to: Entropy Publications, LLC, San Francisco, CA, query@entropypublishing.com. It was Billy's idea to rip off the liquor store. He heard brotherabe on his cell say the place was ripe. Heart of the hood, where this kinda crap happens all the time. And Lucky Liquors is run by this old chink. Gook's at the mart from opening til closing cuz he too damn cheap to hire help from the Projects. Serves him right getting tagged every couple of months. Slide convincing Ty to do the deed. Bluds since Sunshine Daycare, they bled enough and shredded enough to earn respect as the cracka/nigga posse not to jack. Lunchroom Thursday, Billy goes on spouting about taking what they deserve for being dissed since they was kids. From jacking construction sites at seven, to ripping music, movies and apps off the net and selling it on Craigslist at eleven, Tyron is always angling for money. To Ty, it buys respect. He be flipping off his hammered old man and dick-head brother on the way outta town, and his mom too, if she'd stuck around. "One strike gets us a sled and elevates us the rest a high school, blud. Then we outta here, down to Hollywood, man, do some rappin, some actin, be whoever we wanta be, Ty. And even if we get caught, but we won't, the most we'd get is maybe a short stint in juvie since we ain't got no rap sheets. And if we don't get caught, and we won't, I heard Chris say the gets around five large." Tyron stares at Audrey, the hoodrat who brought him out, across the lunchroom, now slumming with the cracka slanger, Baker. "Five grand would get us some respectable treads," Ty says. "We be legally stylin by the weekend if we did the deed this week." "Late afternoon, tomorra," Tyron says. Hoodies and caps, keep our mugs down, away from cameras, and we golden." "We ain't gonna just glide in there and ask for cash, blud. And copin a gun's gonna take time, and it ain't gonna be cheap," Billy feels a need to reality check him. "We don't need no gun. No shit Tyron hated guns. Took his old man out in a drive-by in their driveway when he was nine and his dad's brains landed all over him.


Artificial Intelligence tool could help GPs diagnose heart failure

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A smart stethoscope combined with an AI algorithm could enable earlier diagnosis of heart failure and improved patient outcomes, all at reduced cost. Researchers at the National Heart and Lung Institute and Imperial's Centre for Cardiac Engineering have completed the first-ever NHS study to evaluate an artificial intelligence (AI) technology for point-of-care detection of heart failure. Heart failure is a condition in which the heart cannot pump blood effectively. It carries a higher risk of death than most cancers and is increasingly common, affecting 2% of the UK population and consuming 4% of the NHS budget. Reliably diagnosing heart failure is a major challenge for GPs as the symptoms, such as breathlessness are associated with many other conditions.


How natural language AI could speed patient event reporting

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ECRI and the Institute for Safe Medication Practices PSO know that there were thousands of patient safety events reported in 2021 that will never get reviewed. The patient safety organization is one of about 96 across the country and collects data on mistakes that resulted in patient harm and near misses. This year, member hospitals sent ECRI more than 800,000 of these reports, according to director Sheila Rossi. Federal agencies and PSOs are only able to gain insights from a fraction of events reported every year. Not having the capacity to sift through all the reports has consequences, though it's not required by law.