A new study reveals that during stressful moments in the operating room, surgeons make up to 66 percent more mistakes on patients. Using a technology that captured the electrical activity of a surgeon’s heart, researchers found that during intervals of short-term stress, which can be triggered by a negative thought or a loud noise in the operating room, surgeons are much more prone to make mistakes that can cause bleeding, torn tissue, or burns.
The results of the study, published in the open branch of the British Journal of Surgery, could lead to the development of protocol aiming to reduce acute or short-term stress on surgeons working in the operating room. Medical errors cause between 250,000-440,000 deaths annually in the U.S., with a proportion of those mistakes occurring in operating rooms. Any change in common practice that reduces the number mistakes made by surgeons due to stress would also reduce the number deaths.
It’s an important study published in a prestigious journal, and even more impressive is that its lead author, Peter Dupont Grantcharov, is a master’s student at the Data Science Institute at Columbia. A year and a half ago, Grantcharov had the idea to ask Dr. Homero Rivas, Associate Professor of Surgery at Stanford Medical Center, to wear a Hexoskin Smart Shirt under his scrubs while he did surgeries. The shirt, designed to give athletes precise physiological data during workouts, measures the electrical impulses that trigger heartbeats. From this data, Grantcharov derived heart-rate variability statistics ‒ the variation in times between heartbeats, to determine Rivas’s momentary stress levels.
Science journals are laughing all the way to the bank, locking the results of publicly funded research behind exorbitant paywalls. A campaign to make content free must succeed.
Lately, we’ve been seeing a lot of short cons run by petty grifters who prey on fears to target individuals and small businesses, rather than cities, nations and Fortune 100 multinationals.
Here’s an example: Predictim uses a secret “black-box algorithm” to mine your babysitters’ social media accounts and generate a “risk rating” that you’re entrusting your kid to someone who is a drug abuser, a bully, a harasser, or someone who has a “bad attitude” or is “disrespectful.”
This system does not weed out risky people. It is a modern-day ducking stool, used to brand people as witches. What’s more, it’s a near-certainty that its ranking system is racially biased and also discriminates on the basis of class (because poor and racialized people are overpoliced and more likely to be arrested or otherwise disciplined for offenses that wealthier, whiter people get away with, so if you train a machine-learning system to find the correlates of anti-social behavior, it will just tell you to steer clear of brown people and poor people).
Algorithms used by airlines to split up those travelling together unless they pay more to sit next to each other have been called “exploitative” by a government minister.
Speaking to a parliamentary communications committee, Digital Minister Margot James described the software as “a very cynical, exploitative means… to hoodwink the general public”.
She added: “Some airlines have set an algorithm to identify passengers of the same surname travelling together.
This past summer, the EU launched an initiative to track migration in real time using big data ‒ the masses of machine-readable data each one of us leaves behind every time we use an electronic device.
“The language around this is that it will help refugees,” says Linnet Taylor, a professor and researcher working on data justice. Yet, there’s also a threat.
“Being able to distinguish whether people are Ghanaian, Pakistani or Syrian, for instance, is likely to work against Ghanaians and Pakistanis who may have a perfectly valid claim to asylum, but will be shut out in a world of big data,” says Taylor, hinting at the continent’s migration crisis unfolding in recent years.
Herald & Review, The Southern Illinoisan, Gabriel Neely
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Professor Stella Kantartzi, was hired to head SIU Carbondale’s Soybean Breeding and Genetics Program, with financial assistance from the ISA.
She started with an ISA budget of about $170,000 a year, which she used to establish her lab, and begin research that has led to more than 60 publications and the development of several proprietary soybean varieties, bred for traits like yield and disease resistance.
Back then, it was common for the ISA to fund start-up packages for faculty that it wanted to work with, said Dr. Karen Jones, who chairs Kantartzi’s department at SIUC. That’s part of the reason Meksem, Kantartzi and other researchers came to SIU in the first place.
But by 2011, Kantartzi’s support was down to half its 2008 level.
Last year was California’s most destructive wildfire season on record, with more than 1 million acres burned. This year is breaking different records. The Camp Fire is the deadliest wildfire in California history, with 85 people dead and 249 listed as missing. Officials said the fire, which was 100% contained on Sunday, has also destroyed some 19,000 buildings, most of them homes.
As climate change threatens to expand the size of fires and make fire season an around-the-year event, government agencies, researchers, and companies are turning to AI to cut through a chaos of the data that precedes and comes out of these disasters. The hope is that earlier detection will help firefighters stop them from getting out of hand, aid in recovery, and prevent future fires from starting to begin with.
Artificial intelligence was born of organisational decision-making and state power; it needs human ethics, says Jonnie Penn of the University of Cambridge
To spread misinformation like wildfire, bots will strike a match on social media but then urge people to fan the flames.
Automated Twitter accounts, called bots, helped spread bogus articles during and after the 2016 U.S. presidential election by making the content appear popular enough that human users would trust it and share it more widely, researchers report online November 20 in Nature Communications. Although people have often suggested that bots help drive the spread of misinformation online, this study is one of the first to provide solid evidence for the role that bots play.
The finding suggests that cracking down on devious bots may help fight the fake news epidemic (SN: 3/31/18, p. 14).
Facebook Code; Larry Zitnick, Nafissa Yakubova, Jure Zbontar, Anuroop Sriram
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Facebook AI Research (FAIR) and NYU School of Medicine’s Center for Advanced Imaging Innovation and Research (CAI²R) are sharing new open source tools and data as part of fastMRI, a joint research project to spur development of AI systems to speed MRI scans by up to 10x. Today’s releases include new AI models and baselines for this task (as described in our paper here). It also includes the first large-scale MRI data set of its kind, which can serve as a benchmark for future research.
By sharing a standardized set of AI tools and MRI data, as well as hosting a leaderboard where research teams can compare their results, we aim to help improve diagnostic imaging technology, and eventually increase patients’ access to a powerful and sometimes life-saving technology. With new AI techniques, we hope to generate scans that require much less measurement data to produce the image detail necessary for accurate detection of abnormalities. Sharing this suite of resources reflects the fastMRI mission, which is to engage the larger community of AI and medical imaging researchers rather than to develop proprietary methods for accelerating MR imaging.
Before Amazon’s announcement earlier this month to bring an operations hub to Nashville, college leaders rallied for months to sell the company on the city’s workforce training.
Belmont University’s Bob Fisher told the tech giant last year that there would be a collective effort from universities throughout Middle Tennessee to train future Amazon workers.
“Vanderbilt, Lipscomb and Tennessee State University are all valuable assets,” Fisher said, adding there are also other numerous colleges dotting the area.
Even though Elsevier, which had failed to sign journal subscription contracts with Swedish university libraries over their demands for Open Access, has won a battle against Sci-Hub, an illegal platform for sharing scientific articles, in local courts in Sweden, Bahnhof, a local internet service provider, both complied with the injunction not to offer access to pirated content and effectively counter-blocks local attempts at browsing the websites of Elsevier and the Swedish court.
In the past, NASA space missions have only been available to the public through produced planetarium shows or animations with limited data. A new collaborative effort among Alexander Bock, Moore-Sloan Data Science Fellow at CDS, Charles Hansen, University of Utah, Anders Ynnerman, Linkӧping University, and others aims “to provide an interactive experience in which the public can see and experience space missions to better understand the science, the benefit to mankind, and the challenges of deep-space missions.”
To this end, Bock and collaborators developed OpenSpace, new open source interactive data visualization software which uses existing planetarium capabilities for comprehensive astrovisualization of the entire known universe. OpenSpace uses high-resolution data files called kernels from NASA’s Spacecraft Planet Instrument C-matrix Events (SPICE) to replicate previously flown and planned missions. Full SPICE visualization, in the past, has only been available in mission planning software, produced videos, and some limited commercial applications.
Macau SAR, China April 8-12, 2019. “The annual IEEE International Conference on Data Engineering (ICDE) addresses research issues in designing, building, managing, and evaluating advanced data-intensive systems and applications.” Deadline for submissions is November 30.
Recently, I came up with Thoen’s law. It is an empirical one, based on several years of doing data science projects in different organisations. Here it is: The probability that you have worked on a data science project that failed, approaches one very quickly as the number of projects done grows. I think many, far more than we as a community like to admit, will deal with projects that don’t meet their objectives. This blog does not explore why data science projects have a high risk of failing. Jonathan Nolis already did this adequately. Rather, I’ll look for strategies how we might deal with projects that are failing. Disappointing as they may be, failed projects are inherently part of the novel and challenging discipline data science is in many organisations. The following approach might reduce the probability of failure, but that is not the main point. Their objective is to prevent failing in silence after too long a period of project time. In which you try to figure out things on your own. They will shift failure from the silent personal domain to the public collective one. Hopefully, reducing stress and blame by yourself and others.
We have a situation where we are accumulating more than 500.000 samples of sensor data. The data is appended over time to a huge array.
We want to visualize this growing amount of sensor information as a waveform graph.
To intelligently render this on low-profile hardware, we cannot visit all the data points in a single render pass, but we need to cheat. Instead of drawing lines between all the hundreds of thousands of data points, we draw each vertical pixel column as one line reaching from the minimum sample to the maximum sample.
I’ve seen a lot of failed machine learning models in the course of my work. I’ve worked with a number of organizations to build both models and the teams and culture to support them. And in my experience, the number one reason models fail is because the team failed to create a minimum viable product (MVP).
In fact, skipping the MVP phase of product development is how one legacy corporation ended up dissolving its entire analytics team. The nascent team followed the lead of its manager and chose to use a NoSQL database, despite the fact no one on the team had NoSQL expertise. The team built a model, then attempted to scale the application. However, because it tried to scale its product using technology that was inappropriate for the use case, it never delivered a product to its customers. The company leadership never saw a return on its investment and concluded that investing in a data initiative was too risky and unpredictable.