Using large healthcare encounter datasets, a machine learning algorithm is able to identify patients with a common genetic disorder that carries a high risk for early heart attacks and strokes. While individuals with familial hypercholesterolaemia (FH) have 20 times the risk of developing cardiovascular disease than the general population, fewer than 10 percent of the 1.3 million Americans born with the genetic disease are diagnosed. "People born with familial hypercholesterolemia develop cardiovascular damage by puberty, often culminating in early heart attacks or the need for surgery as young or middle-aged adults," says Katherine Wilemon, founder and CEO of the FH Foundation, a non-profit research and advocacy organization. "Since diagnosis of this deadly but treatable condition has stalled in the American medical system, the FH Foundation harnessed artificial intelligence and big data to accelerate identification of those most likely to have FH." In a new study, a machine learning model created by the FH Foundation successfully leveraged healthcare encounter databases to identify individuals with the genetic disorder.

Continuing Our series of posts on how to interpret Machine Learning algorithms and predictions. Part 0 (optional) -- What is Data Science and the Data Scientist Part 1 -- Introduction to Interpretability Part 1.5 (optional) -- A Brief History of Statistics (May be useful to understand this post) Part 2 -- (this post) Interpreting models of high bias and low variance. Part 4 -- Is it possible to resolve the trade-off between bias and variance? Using Shapley to finally open the black box! In this post we will focus on the interpretation of high bias and low variance models, as we explained in the previous post, these algorithms are the easiest to interpret so assume several prerequisites in the data.

Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers' unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to ...

Our recent research indicates that patients are reluctant to use health care provided by medical artificial intelligence even when it outperforms human doctors. Because patients believe that their medical needs are unique and cannot be adequately addressed by algorithms. To realize the many advantages and cost savings that medical AI promises, care providers must find ways to overcome these misgivings. Medical artificial intelligence (AI) can perform with expert-level accuracy and deliver cost-effective care at scale. IBM's Watson diagnoses heart disease better than cardiologists do.

At U.S. Bank, we're passionate about helping customers and the communities where we live and work. The fifth-largest bank in the United States, we're one of the country's most respected, innovative and successful financial institutions. U.S. Bank is an equal opportunity employer committed to creating a diverse workforce. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, disability or veteran status, among other factors. U.S. Bank is seeking a proficient Big Data ETL Architect with experience in Big Data technologies and Data Architecture to contribute toward the success of our technology initiatives.

A new computational tool developed by KAUST scientists uses artificial intelligence (AI) to infer the RNA-binding properties of proteins. The software, called NucleicNet, outperforms other algorithmic models of its kind and provides additional biological insights that could aid in drug design and development. "RNA binding is a fundamental feature of many proteins," says Jordy Homing Lam, a former research associate at KAUST and co-first author of the study. "Our structure-based computational framework can reveal the detailed RNA-binding properties of these proteins, which is important for characterizing the pathology of many diseases." Proteins routinely interface with RNA molecules as a way to control the processing and transporting of gene transcripts--and when these interactions go awry, information flow inside the cell is disrupted and disorders can arise, including cancer and neurodegenerative disease.

Before we delve deep into this subject, let's hear what expert-level research has to say about both of these technologies: We can keep on going with the many more remarkable statistics about AI and IoT, but these ones should be enough for now. The re-emergence of the decades-old technological ideas like Artificial Intelligence and the Internet of Things, at the right time and right place, has suddenly disrupted the traditional industrial norms – for the better this time. It has kickstarted a digital revolution that was only possible way back in the science fiction writings of H.G. Wells, Jules Verne, Arthur Conan Doyle, or other masterminds of Sci-Fis. It has ushered the classical Industrial Revolution of the 18th century into the Industry 4.0 of the 21st. The early proponents and experts of both technologies were simply ecstatic about the outstanding transformational possibilities a union between AI and IoT could produce.

It can outperform radiologists when screening for lung cancer. And it can even detect post-traumatic stress disorder in veterans by analyzing voice recordings. It sounds like a page from science fiction--but studies issued during the past year alone have claimed AI can do all of the above, and more. Early findings like those are raising interest in AI's potential to overhaul patient care as we know it. Top healthcare CEOs are eyeing the space, with nearly 90% of CEOs indicating they've seen AI developers targeting clinical practice, according to a Power Panel survey Modern Healthcare conducted this year.

As we near the end of the year, it's fair to say that 2019 has been a massive year, with sweeping new changes across most major industries. With so many emerging markets, we're seeing a substantial evolution in everything from technology (that computer-altered scene from the Shining, where they seamlessly replaced Jack Nicholson with Jim Carrey, blew everyone's minds when it first came out online) to developing sustainable methods of producing meat (if you live in Canada, many fast food restaurant chains from A&W to Tim Hortons now offers motherless meat substitutes, like Beyond Meat, in their sandwiches as an alternative). And then, there are controversial discussions around the legalization, wholesale distribution and non-medical retail presence of cannabis and its related consumer goods, like CBD. Taking the time to look at some of these "hot-trend" initiatives across North America's business world can be crucial to understanding their current (or future) economic impact. Our team at GURUS have put together a list of 10 big market trends that are hot topics for business leaders and investors going into 2020.