Collaborating Authors

Cardiology/Vascular Diseases

Stroke Prediction using Data Analytics and Machine Learning


Stroke is the second leading cause of death worldwide. According to the World Health Organization [1], 5 million people worldwide suffer a stroke every year. Of these, one third die and another third are left permanently disabled. In the United States, someone has a stroke every 40 seconds and every four minutes, someone dies [2]. The aftermath is devastating, with victims experiencing a wide range of disabling symptoms including sudden paralysis, speech loss or blindness due to blood flow interruption in the brain [3].

'Tech for Good': Using technology to smooth disruption and improve well-being


The development and adoption of advanced technologies including smart automation and artificial intelligence has the potential not only to raise productivity and GDP growth but also to improve well-being more broadly, including through healthier life and longevity and more leisure. Alongside such benefits, these technologies also have the potential to reduce disruption and the potentially destabilizing effects on society arising from their adoption. Tech for Good: Smoothing disruption, improving well-being (PDF–1MB) examines the factors that can help society achieve such benefits and makes a first attempt to calculate the impact of technology adoption on welfare growth beyond GDP. Our modeling suggests that good outcomes for the economy overall and for individual well-being come about when technology adoption is focused on innovation-led growth rather than purely on labor reduction and cost savings through automation. This needs to be accompanied by proactive transition management that increases labor market fluidity and equips workers with new skills. Technology for centuries has both excited the human imagination and prompted fears about its effects. Today's technology cycle is no different, provoking a broad spectrum of hopes and fears.

12 of the best suspense movies on Netflix to put you on edge


Given how stress-inducing the real world can be at the best of times, movies that rely on tension as a driving force might seem like an odd entertainment choice to some. But who are we to judge? Maybe spending 90 minutes sweating and wincing in front of the TV screen is actually a cathartic way to let off some steam -- and at the very least, a suspense movie is always a great way to get the ol' heart-rate up. But what actually is a suspense movie? How is it different to a thriller?

Organizations launch program to improve AF management in underserved areas


The American College of Cardiology, the Heart Rhythm Society and the Bristol Myers Squibb/Pfizer alliance announced they have launched a program to improve management of atrial fibrillation in underserved communities. The TRANSFORM: Atrial Fibrillation Quality Initiative (TRANSFORM: AF) will give resources to providers and health care systems to improve adoption of guideline-directed medical therapies in patients from underserved communities, the organizations stated in a press release. The TRANSFORM: AF program will provide a set of tools to manage AF by leveraging telehealth and remote monitoring tools, according to the release. The ACC and HRS will conduct a baseline assessment supported by HealthReveal, a provider of artificial intelligence, to identify high-risk patient populations from underserved communities, and data from the assessment will be used to address gaps in care and to recommend patient care interventions, according to the release. "Early and effective treatment is critical for appropriate management of AF, but some communities are disproportionately impacted by this disease because of low rates of early intervention," James Januzzi, MD, TRANSFORM: AF co-chair, a member of the ACC board of trustees and director of the Dennis and Marilyn Barry Fellowship in Cardiology Research at Massachusetts General Hospital, said in the release.

Multimodal AI in Healthcare: Closing the Gaps


Healthcare professionals, in their daily routine, make use of multiple sources of data. To arrive to a diagnosis and decide on patient management, they rely on a combination of several types and sources of data: imaging (e.g., Radiology, Pathology, Ophthalmology), time series (e.g., electrocardiograms -- ECG), structured clinical data (e.g., vital signs, lab results) and non-structured data (e.g., clinical notes). Considering the level of expertise required to understand in depth one single data type, it is close to impossible for a single healthcare professional to master all areas. A radiologist has specialized training to read radiological images, but doesn't know as much about internal medicine or surgery. A cardiologist has a deep understanding of ECGs, but normally does not know how to evaluate a pathology slide.

AI to improve stroke care at Barking, Havering and Redbridge


Barking, Havering and Redbridge University Hospitals NHS Trust is improving its response to stroke care with the launch of new software which uses artificial intelligence. The Brainomix software acts as a second opinion by analysing CT images of the brain and blood vessels, and automatically highlighting blocked blood vessels to indicate possible areas of damage. It also means stroke teams will be able to easily share scanned images to aid rapid diagnosis and support clinical decisions and treatments. Amanda Martin, stroke co-clinical lead at the trust, said: "At a local level, this decision support tool will help us to speed up diagnosis and therefore patient care in a simple and safe way… we are hoping that the implementation of Brainomix will support the highly specialised thrombectomy pathway, facilitating the timely transfer of those eligible for treatment to the trust." The AI technology can be used as a mobile app, meaning clinical decisions can be made swiftly and from anywhere. It will also connect the trust's stroke team with colleagues at University College London Hospitals NHS Foundation Trust and Barts Health NHS Trust and provide a 24/7 service.

Artificial intelligence predicts brain age from EEG signals recorded during sleep studies


A study shows that a deep neural network model can accurately predict the brain age of healthy patients based on electroencephalogram data recorded during an overnight sleep study, and EEG-predicted brain age indices display unique characteristics within populations with different diseases. The study found that the model predicted age with a mean absolute error of only 4.6 years. There was a statistically significant relationship between the Absolute Brain Age Index and epilepsy and seizure disorders, stroke, elevated markers of sleep-disordered breathing (i.e., apnea-hypopnea index and arousal index), and low sleep efficiency. The study also found that patients with diabetes, depression, severe excessive daytime sleepiness, hypertension, and/or memory and concentration problems showed, on average, an elevated Brain Age Index compared with the healthy population sample. According to the authors, the results demonstrate that these health conditions are associated with deviations of one's predicted age from one's chronological age.

Artificial Intelligence Improves The Lives Of Patients, Doctors, And Hospital Administrators By Performing Tasks That Humans Could Normally Perform In A Fraction Of Time. Here Is How Artificial Intelligence Revolutionize The Digital Health Monitoring System


We have enough problems these days. The last thing we need to worry about is our health or the high costs of care. That's why many people are turning to digital health solutions. As noted by The Wall Street Journal, many of these solutions can reportedly help manage diabetes, improve sleep, monitor heart health, encourage weight loss, track whether patients are sticking to physical therapy regimens, and more.(3) Due to the recent health scare, governments and healthcare systems worldwide may now realize how essential digital health has become.

AWS leader talks about technologies needed to take precision medicine to the next level


One of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics, supporting organizations as they work to uncover unique datasets to find novel associations and signals. By supporting greater interoperability and collaboration, data scientists, developers, clinicians and pharmaceutical partners have the opportunity to leverage machine learning to reduce the time it takes to move from insight to discovery, ultimately leading to the right patients receiving the right care, with the right therapeutic at the right time. To get a better understanding of challenges surrounding precision medicine and its future, Healthcare IT News sat down with Taha Kass-Hout, director of machine learning at AWS. Q: You've said that one of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics. Please explain this challenge in detail. A: One of the challenges in developing and utilizing storage, analytics and interpretive methods is the sheer volume of biomedical data that needs to be transformed that often resides on multiple systems and in multiple formats.

What Artificial Intelligence Can Do for Stroke Patients


Baylor St. Luke's Is First in Houston To Adopt Artificial Intelligence for Stroke Care As one of the leaders in stroke care in Houston and the surrounding areas, Baylor St. Luke's Medical Center continues on its promise to leverage the most advanced innovations to provide the best care to its patients. Baylor St. Luke's invested in artificial intelligence technology to service the stroke care team in diagnosing stroke and providing efficient and reliable treatment. technology allows for rapid detection and notification of suspected large vessel occlusion (LVO) strokes. Chethan P Venkatasubba Rao, Medical Director of the Neuroscience ICU at Baylor St. Luke's With Stroke, Timing Is Everything During a stroke, timing is the most important factor for minimizing brain damage. Knowing the signs of a stroke will allow for F.A.S.T. action in the event of an emergency.