Investigators have identified characteristics of individuals with long COVID and those who are likely to have it by using machine learning techniques. The investigators, who were supported by the National Institutes of Health (NHI), analyzed a collection of electronic health records (EHR) available for COVID-19 research to help better identify who has long COVID. Investigators used the EHR data, from the National COVID Cohort Collaborative (N3C), a centralized national public database led by the NIH's National Centers for Advancing Translation Sciences, to identify more than 100,000 likely cases of long COVID, as of October 2021 and 200,000 cases as of May 2022. "It made sense to take advantage of modern data analysis tools and a unique big data resource like N3C, where many features of long COVID can be represented," Emily Pfaff, PhD, a clinical informaticist at the University of North Carolina at Chapel Hill, said in a statement. The N3C data includes information representing more than 13 million individuals nationwide and nearly 5 million positive COVID-19 cases.
Artificial intelligence in healthcare has come a long way. The use of computers has advanced significantly over the past few years. Today, sophisticated machines have been developed to perform human tasks like analyzing and interpreting data and assisting with problem-solving. While machine learning (ML) has been widely used in many industries, the use and application of Artificial Intelligence (AI) in healthcare is still relatively new. It is only recently that we have seen AI move from the world of academics and research laboratories to hospitals.
BackgroundHandwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.Materials and MethodsOne-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.ResultsStroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and A...
Artificial intelligence (AI) is the imitation of human intelligence progressions by machines, mainly computer systems. Artificial intelligence has extensive applications in the healthcare sector. AI solutions assist healthcare providers in several aspects of patient care and administrative processes. Medical imaging can be defined as the diagnostic procedure that encompasses the formation of visual assistance and image representations of the human body and includes the monitoring of the execution and working of the organs of the human body. Artificial intelligence primarily consists of two types, machine learning and robots.
It is predicted that technologies such as artificial intelligence (AI), cloud computing, extended reality and the Internet of Things (IoT) will be introduced further among related workers, leading to the development and provision of new and better treatments and services. In the months following the outbreak of the COVID-19 outbreak, the proportion of telemedicine consulting has risen sharply from 0.1% to 43.5%, and is expected to rise further in the future, as this trend could save more patients' lives, said Deloitte Accounting Firm analyst. . To achieve this goal, the next-generation portable device, heart rate, stress, and blood oximetry, enables doctors to accurately determine the patient's condition in real time. During the COVID-19 period, doctors built'virtual hospital rooms' in some areas to observe the treatment status of patients in various areas through the central communication infrastructure. The Pennsylvania Emergency Medical Center is developing a high-quality'virtual emergency room'.
Abstract: Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Abstract: How to identify and characterize functional brain networks (BN) is fundamental to gain system-level insights into the mechanisms of brain organizational architecture. Current functional magnetic resonance (fMRI) analysis highly relies on prior knowledge of specific patterns in either spatial (e.g., resting-state network) or temporal (e.g., task stimulus) domain.
AI has made impressive strides in recent years, but it's still far from learning language as efficiently as humans. For instance, children learn that "orange" can refer to both a fruit and color from a few examples, but modern AI systems can't do this nearly as efficiently as people. This has led many researchers to wonder: Can studying the human brain help to build AI systems that can learn and reason like people do? Today, Meta AI is announcing a long-term research initiative to better understand how the human brain processes language. In collaboration with neuroimaging center Neurospin (CEA) and INRIA we're comparing how AI language models and the brain respond to the same spoken or written sentences.