Wearable activity trackers that monitor changes in skin temperature and heart and breathing rates, combined with artificial intelligence (AI), might be used to pick up COVID-19 infection days before symptoms start, suggests preliminary research published in the open access journal BMJ Open. The researchers base their findings on wearers of the AVA bracelet, a regulated and commercially available fertility tracker that monitors breathing rate, heart rate, heart rate variability, wrist skin temperature and blood flow, as well as sleep quantity and quality. Typical COVID-19 symptoms may take several days after infection before they appear during which time an infected person can unwittingly spread the virus. Attention has started to focus on the potential of activity trackers and smartwatches to detect all stages of COVID-19 infection in the body from incubation to recovery, with the aim of facilitating early isolation and testing of those with the infection. The researchers therefore wanted to see if physiological changes, monitored by an activity tracker, could be used to develop a machine learning algorithm to detect COVID-19 infection before the start of symptoms.
Health trackers worn on the wrist could be used to spot Covid-19 days before any symptoms appear, according to researchers. Growing numbers of people worldwide use the devices to monitor changes in skin temperature, heart and breathing rates. Now a new study shows that this data could be combined with artificial intelligence (AI) to diagnose Covid-19 even before the first tell-tale signs of the disease appear. "Wearable sensor technology can enable Covid-19 detection during the presymptomatic period," the researchers concluded. The findings were published in the journal BMJ Open.
Background: Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes. Objective: The aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients. Methods: The electronic health record data set included 40,703 patient encounters from 7 hospitals from April 20, 2018, to March 17, 2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals.
Posted on June 7th, 2022 by Lawrence Tabak, D.D.S., Ph.D. The COVID-19 pandemic continues to present considerable public health challenges in the United States and around the globe. One of the most puzzling is why many people who get over an initial and often relatively mild COVID illness later develop new and potentially debilitating symptoms. These symptoms run the gamut including fatigue, shortness of breath, brain fog, anxiety, and gastrointestinal trouble. People understandably want answers to help them manage this complex condition referred to as Long COVID syndrome. But because Long COVID is so variable from person to person, it's extremely difficult to work backwards and determine what these people had in common that might have made them susceptible to Long COVID.
Scientific research tanks like Gartner say up to 80 percent of customer interactions are managed by AI today. In 2020, Statista stated that AI handled 54 percent of customers' daily interactions with their favorite organizations or stores. More of this will help you predict customers' preferences, hook them, turn visitors into customers and make their shopping experiences more accessible. Servion Global Solutions predicted that AI would empower 95% of customer interactions by 2025 back in 2017. Since the COVID-19 pandemic happened, this figure is more certain.
Artificial intelligence and machine learning promise to transform healthcare across the board, but particularly through the use of precision medicine. Precision medicine is often defined differently than the common phrase "personalized medicine," which simply means tailoring treatments to the patient. Precision medicine, on the other hand, specifically applies machine learning to the genetic material of patients with less-common conditions. The AI finds patterns within material to identify common phenotypes, while pharmaceutical companies use that information to develop drugs targeted to the specific need. Palo Alto, California-based Endpoint Health is one player in this space looking to tap the potential machine learning has for precision medicine.
A group of Northeastern researchers is tapping into the power of machine learning to develop new models for identifying patients who may have post-acute sequelae of SARS-CoV-2 infection, or so-called "long COVID." Using electronic health records from the National COVID Cohort Collaborative, a federal database that compiles medical information about COVID-19 patients, researchers were able to develop models that helped identify COVID long haulers across a range of features--from past COVID diagnosis, to the types of medications they've been prescribed, according to new research published in Lancet Digital Health. The data harmonization effort drew from a variety of information sources to construct a picture of what long COVID looks like in the U.S.--and who is most likely to have it. Those sources include demographic data, healthcare visit details, diagnoses and medications for 97,995 adults with COVID-19, the study says. Patients most likely suffering from the post-infection illness, which is estimated to plague between 10-30% of people who contract COVID-19, are often characterized as having new or lingering symptoms that are present 90 days after being diagnosed with the viral infection--a criteria researchers also used to determine their base population in their analysis.
This article is a collaboration with David Gossett, Principal with Infornautics, who builds first mover technologies that have no instruction set and need to be invented from scratch. He believes data has a story to tell if we apply the right machine models. His specialty is unstructured data. This article is intended to be provocative, to summon curiosity into the issues that plague us today when it comes to machine learning. Three years ago, I wrote this article, Artificial Intelligence Needs to Reset. The AI Hype that was supposed to transpire into all-things automated is still far off. Since that time, we've experienced speed bumps that have pointed to issues including lack of model accountability (black boxes), bias, lack of data representation in the training set etc. An AI Ethics movement emerged to demand more responsible tech, increased model transparency and verifiable models that do what they're supposed to do without impairment or harm to individuals or groups, in the process. Our future is Artificial Intelligence. It's been conjectured that this wonderful AI will be our savior.
Clinical scientists have explored de-identified electronic health record data in the National COVID Cohort Collaborative(N3C), a National Institutes of Health-funded national clinical database, using machine learning models to help decipher characteristics of individuals with long COVID and attributes that may help identify such patients using information from medical records. The discoveries published in The Lancet Digital Health have the potential to enhance clinical research on extended COVID and inspire a more consistent COVID treatment regimen. The author Emily R. Pfaff, Ph.D., an assistant professor in the UNC School of Medicine's Division of Endocrinology and Metabolism, said that characterizing, diagnosing, treating, and caring for long COVID patients has turned out to be difficult owing to the list of characteristic symptoms constantly evolving over time. They needed to better grasp the intricacies of long COVID, and it made sense to use current data analysis methods and a unique, extensive data resource like N3C, which represents many of the properties of long COVID. The N3C data enclave, funded by the National Institutes of Health's National Center for Advancing Translational Sciences (NCATS), already has information on more than 13 million people from 72 locations, including approximately 5 million COVID-19-positive patients.
Artificial Intelligence tools and applications have skillfully tried to manage the analysis, diagnosis, tracing, and development of the pandemic in ways unthinkable with manpower solely. The greatest dilemma with this pandemic was that no one knew what it was and how it would react during the beginning of the pandemic. To make matters worse, Covid 19 has been rapidly mutating since its start, and researchers around the world aren't still quite prepared to interact with such a delicate mutating variant that has claimed hundreds and thousands of lives and has essentially changed the course of history forever. This is where AI's prowess comes into play. With deep learning and the combination of researchers from all around the world, Artificial Intelligence has helped us combat the pandemic in unimaginable ways. The foremost task of AI was to collect as much data as possible about the Coronavirus.