Machine learning-based ASCVD risk calculator outperforms ACC/AHA standard


A machine learning (ML)-based risk calculator developed to assess an individual's long-term risk for atherosclerotic cardiovascular disease (ASCVD) identified 13 percent more high-risk patients and recommended unnecessary statin therapy 25 percent less often than standard risk assessment tools in initial tests, researchers reported in the Journal of the American Heart Association. First author Ioannis A. Kakadiaris, PhD, and colleagues with the Society for Heart Attack Prevention and Eradication (SHAPE) wrote in JAHA that the current gold standard for ASCVD risk assessment--the American College of Cardiology and American Heart Association's Pooled Cohort Equations Risk Calculator--is flawed in its accuracy. "Studies have demonstrated that the current U.S. guidelines based on the ACC/AHA risk calculator may underestimate risk of atherosclerotic CVD in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events," Kakadiaris and coauthors said. The existing approach to CVD risk assessment desperately needs an overhaul." According to a consensus report from SHAPE, comprehensive ASCVD risk assessment should include evaluation of plaque, blood and myocardial vulnerability factors if it's going to be anywhere near accurate.

Artificial Intelligence raises ethical, policy challenges – UN expert


While these bring tremendous benefits, AI also raises concerns, ranging from security, to human rights abuses. Speaking in Paris last weekend, Secretary-General Antonio Guterres praised AI but cautioned that "technology should empower not overpower us" and that the world needs to set policies that contain unintended consequences or malicious use of frontier technologies. UN News asked Eleonore Pauwels, Research Fellow on Emerging Cybertechnologies at United Nations University (UNU), about AI – what it is, how it works, and what she sees happening in the next few years. In its current form, called "deep learning", AI is a growing set of autonomous and self-learning algorithms she told us, capable of performing tasks it was commonly thought could only be done by the human brain. At its core, AI produces powerful predictive reasoning while minimizing the noise from unpredictable and complex human behaviour.

Here's what you need to know before going under the robo-knife

Daily Mail

First, you are strapped from the chest upwards on to the table, with your feet hoisted into stirrups. The table is swung down backwards, so you are tilted, head-down, at an angle of 45 degrees. Then a machine, known by some surgeons as'the 800lb gorilla', can get to work. It sounds so medieval, but this is the most modern of surgical techniques -- robotic surgery. The extraordinary posture, known as the steep Trendelenburg, is necessary to position the patient precisely so the robot arms can reach inside them.

What If AI Could Uber The Healthcare Industry?


I am never cleanly shaven. Or because I don't care about my appearance. Three years ago, I stopped shaving with water to conserve H20. It might seem like a little thing, but little things add up. When I tell people this, they sometimes scoff at me. "Oh, come on," they say.

AI predicts risk of death from heart disease more accurately than experts


Scientists have designed a model using Artificial Intelligence that can predict risk of death in patients with coronary heart disease (CHD) better than expert-constructed models. According to a new study published in PLOS One, scientists from the Francis Crick Institute, working with University College London Hospitals NHS Foundation Trust and the Farr Institute of Health Informatics Research, developed the AI model using the data of 80,000 patients, available for researchers through UCL's CALIBER platform, which links four sources of electronic health data in England. The model that the AI one was compared to made predictions based on 27 variables chosen by medical experts, while the Crick team got their AI algorithms to train themselves, look for patterns and select the most relevant variables from a set of 600. Both machine learning and AI are picking up steam in healthcare, with hospitals testing or deploying the tech for a range of use cases from treating patients with pancreatic cancer to reducing surgical site infections while experts are saying the next generation of clinical decision support tools will include AI in workflow to improve diagnostics, imaging, radiology and pathology, among other functions. Consultancy McKinsey said last month that hospitals need a solid digital base comprising a modern infrastructure with cloud, mobile and web capabilities in place before starting down the road to AI and machine learning.

Using Big Data to Give Patients Control of Their Own Health


Big data, personalized medicine, artificial intelligence. String these three buzzphrases together, and what do you have? A system that may revolutionize the future of healthcare, by bringing sophisticated health data directly to patients for them to ponder, digest, and act upon--and potentially stop diseases in their tracks. At Singularity University's Exponential Medicine conference in San Diego this week, Dr. Ran Balicer, director of the Clalit Research Institute in Israel, painted a futuristic picture of how big data can merge with personalized healthcare into an app-based system in which the patient is in control. Picture this: instead of going to a physician with your ailments, your doctor calls you with some bad news: "Within six hours, you're going to have a heart attack. So why don't you come into the clinic and we can fix that."

Real-world data, machine learning, and the reemergence of humanism


As we relentlessly enter information into our EHRs, we typically perceive that we are just recording information about our patients to provide continuity of care and have an accurate representation of what was done. While that is true, the information we record is now increasingly being examined for many additional purposes. A whole new area of study has emerged over the last few years known as "real-world data," and innovators are beginning to explore how machine learning (currently employed in other areas by such companies as Amazon and Google) may be used to improve the care of patients. The information we are putting into our EHRs is being translated into discrete data and is then combined with data from labs, pharmacies, and claims databases to examine how medications actually work when used in the wide and wild world of practice. Let's first talk about why real-world data are important.

AI technology in healthcare advances surgical, clinical support


Healthcare has long considered technology as essential in improving the treatment and care of patients. With AI ushering in what's said to be the fourth industrial revolution, many hospitals are gearing up for new AI-based applications that will further improve patient outcomes, increase physician productivity and reduce errors. Based on several studies, AI-based applications can potentially save the U.S. healthcare industry $150 billion annually by 2026. As AI technology in healthcare continues to gain wider acceptance, several areas are predicted to experience the most success and make the greatest impact. Hospitals find it challenging to recruit nurses in several parts of the U.S., and in some rural areas, the nursing shortage is affecting patient care.

How AI can inspire doctors to be more inventive


The entirety of clinical science has been shaped solely by an historic ability to measure something. We measure blood pressure only because 150 years ago someone found that they could, without at first understanding the full implications. A decade or so later, doctors showed that individuals with high blood pressures were having more life-threatening health events than those with a lower value, and this in turn encouraged the development of drugs that could lower high blood pressure. Several years later, conclusive evidence arrived that these agents could reduce life-threatening cardiovascular events, including stroke, fatal heart attack and kidney failure. And so a whole field of clinical science and an associated industry of diagnostics, investigation and treatment evolved from this initial enquiry into blood pressure.

Mayo, Eko team on machine learning to detect heart abnormalities


The Mayo Clinic and health technology vendor Eko are working together to develop and commercialize a machine learning-based algorithm that screens patients for low ejection fraction, which is linked to heart failure. A low ejection fraction number, often measured by an echocardiogram, suggests problems with the heart's pumping function. However, echocardiography is an expensive and time-consuming medical imaging test using ultrasound that is less accessible than a doctor with a stethoscope. "With this collaboration, we hope to transform the stethoscope in the pocket of every physician and nurse from a hand tool to a power tool," said Paul Friedman, MD, chair of cardiovascular medicine at the Mayo Clinic. "The community practitioner performing high school sports physicals and the surgeon about to operate may be able to seamlessly tap the knowledge of an experienced cardiologist to determine if a weak heart pump is present simply by putting a stethoscope on a person's chest for a few seconds."