from the world's big
An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.
- 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
- Early treatment is available but there's been no way to tell who needs it.
- Using clinical data already being collected, machine learning can identify who's at risk.
The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.
In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.
That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.
70 data points and machine learning
Image source: Creators Collective/Unsplash
Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:
"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."
The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.
Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."
Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.
Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.
On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.
Image source: Külli Kittus/Unsplash
Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."
"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.
The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.
The Omni Calculator site is a stunning treasure trove of free calculators.
- 1,175 calculators attempt to solve every everyday math problem for you.
- All free to use, it's amazing how many aspects of life get a calculator.
- Bookmark this collection — it's hard to imagine you won't someday need it.
It's true that high-school calculus teachers torture their students with them, but it's also true that once some degree of mastery is in hand. Mathematicians love a good — efficient, clever, and useful — formula.
These things aren't just for classrooms or advanced scientific applications, either. While it's amazing that formulas predict what will happen if we slingshot a spacecraft around some distant celestial body, they can also be part of our earthly lives calculating all sorts of everyday things.
In any event, for many math heads (carefully typed), slinging formulas together and inventing new calculators is just plain fun. Last week, for example, UK physicist Steven Wooding sent us the link to a calculator he and a friend constructed that predicts contactable alien civilizations. That was fun, but the site to which he directed us is nothing short of dazzling: It's called Omni Calculator, and it's a mind boggling repository of 1,175 calculators whose purpose is to help everyone get to the right answers in their personal and professional lives.
A mathematical treasure chest
Image source: Alexey Godzenko/Shutterstock
Want to know exactly how many balloons it would take to send your house airborne, as in the Pixar's "Up"? No problem. Hate running unexpectedly out of toothpaste en route to bed? Live your best life. Ditto toilet paper.
Some of the calculators are pretty profound, too, such as the Every Second calculator that shows just how much happens in the world every 60th of a minute — it's an enthralling set of numbers.
Fun stuff aside, Omni Calculator is an absolutely staggering collection, an incredible resource for normal people and professionals—from doctors, to chemists, to financial advisers, to construction teams, and more.
Who is behind Omni Calculator?
Image source: rawf8/Shutterstock
Omni Calculator is the project of a Polish startup of 24 people dedicated to helping others solve all of the small math problems in their daily lives. The company manifesto:
"In a surprisingly large part, our reality consists of calculable problems. Should I buy or rent? What's my ideal calorie intake? Can I afford to take this loan? How many lemonades do I need to sell in order to break even? Often times we don't solve these problems, because we lack knowledge, skills, time or willingness to calculate. And then we make bad, uninformed decisions?"
Omni Calculator is here to change all that — we are working on a technology that will turn every* calculation-based problem trivial to solve for anyone.
The asterisk says, "within reason."
It all started when founder Mateusz Mucha built a unique web calculator. It could calculate in any direction without a fixed input or output. He invested $80 in translating his Percentage Calculator into 15 languages and stood back as the app was downloaded 4 million times, and counting.
At some point Mucha changed his goal: "Instead of calculating one thing, we'll calculate all of them — for everybody." To serve this aim, all of Omni Calculator's calculators are free to use, developed by the company in collaboration with all sorts of experts.
Go spend some time looking around and bookmarking tools for your own use. You're pretty much guaranteed to find something that solves a problem with which you're struggling. At the very least you'll come across some amazing calculators that will get you thinking about unexpected things.
Omni Calculator also provides a special set of calculators that allow you to crunch COVID-19 numbers for yourself, from a social distancing calculator to one that can predict when your next stimulus check should be due.
Some of the world's top minds weigh in on one of the most divisive questions in tech.
- When it comes to the question of whether AI is an existential threat to the human species, you have Elon Musk in one corner, Steven Pinker in another, and a host of incredible minds somewhere in between.
- In this video, a handful of those great minds—Elon Musk, Steven Pinker, Michio Kaku, Max Tegmark, Luis Perez-Breva, Joscha Bach and Sophia the Robot herself—weigh in on the many nuances of the debate and the degree to which AI is a threat to humanity; if it's not a species-level threat, it will still upend our world as we know it.
- What's your take on this debate? Let us know in the comments!
Mathematicians studied 100 billion tweets to help computer algorithms better understand our colloquial digital communication.
- A group of mathematicians from the University of Vermont used Twitter to examine how young people intentionally stretch out words in text for digital communication.
- Analyzing the language in roughly 100 billion tweets generated over eight years, the team developed two measurements to assess patterns in the tweets: balance and stretch.
- The words people stretch are not arbitrary but rather have patterned distributions such as what part of the word is stretched or how much it stretches out.
Balance and Stretch<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMzM2NTg3My9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxMTEwMjM4NH0.P2pcKvbcsKKi8_0RTsDrsIABnxSHybXZYOLxHYT-KZk/img.jpg?width=1245&coordinates=6%2C0%2C6%2C0&height=700" id="df914" class="rm-shortcode" data-rm-shortcode-id="ddc7d5797ec2a42182452a971813111e" data-rm-shortcode-name="rebelmouse-image" />
Photo credit: Dole777 / Unsplash<p>Over the last two decades, social media has provided scientists with a trove of free information about human behavior and language. A group of mathematicians from the University of Vermont used Twitter to examine how young people intentionally stretch out words in text for digital communication. They created a method to essentially quantify the semantic nuances in between stretched words, like "right" vs. "riiiiiight," with the aim to teach future AI algorithms human digital colloquialisms.</p><p>"Written communication has recently begun encoding new forms of expression, including the emotional emphasis delivered by stretching words out," <a href="https://www.techrepublic.com/article/sayyy-whatttt-researchers-analyze-strange-human-tweets-to-build-better-ai/" target="_blank">said Chris Danforth</a>, professor of Mathematics & Statistics in the Vermont Complex Systems Center and member of the research team behind the study.</p><p>In their study, published last week in the journal PLOS One, the team analyzed the language in roughly 100 billion tweets generated from 2008 to 2016. They developed two measurements to assess patterns in the tweets: balance and stretch. For example hahahaha would be considered a stretched world high on balance while a term like wtffffff has stretch but little balance as only one letter, f, contributes to the stretchiness. This means to put emphasis on the world abbreviated by the letter "f". </p><p>"With so much communication happening electronically these days, we're all trying to find ways to convey emotion through text. Emojis are helping, but the visual effect of 30 consecutive vowels in a curse word turns a bland profanity into a form of art," Danforth said.</p><p>Interestingly, the use of elongated words was found across languages. For example, "kkkkkkk" signifies laughter in Brazilian Portuguese while "wkwkwkwkwkwk" expresses it in Indonesian, according to the researchers. </p>
Beyond the dictionary<p>Ultimately, this project could help artificial intelligence algorithms understand critical intrinsic meanings contained in the idiosyncratic variations in our communicative text or other linguistic symbols, such as punctuation and emojis.</p> <p>Dictionary definitions hardly reflect the way that we actually communicate with one another digitally. What the researchers found, though, is that the words people stretch out aren't arbitrary. Rather, they have patterned distributions such as what part of the word is stretched or how much it stretches out. Colloquial digital language is, after all, a system of symbols and for it to transfer meaning we must all be "in" on the patterns. </p> <p>This research suggests that by gaining understanding into stretched words used on social media opens more doors to helping AI better understand our slang. Tools and methods were developed that could be useful in future studies, for example investigations of intentional mis-typings and misspellings. </p> <p>What benefits come from AI algorithms better understanding our digital lingo? For one, it's possible that new tools could be applied to improve natural language processing, search engines, and spam filters. </p> <p>"We were able to comprehensively collect and count stretched words like 'gooooooaaaalll' and 'hahahaha'," the researchers <a href="https://www.sciencedaily.com/releases/2020/05/200527150155.htm" target="_blank">said in a press release</a>, "and map them across the two dimensions of overall stretchiness and balance of stretch, while developing new tools that will also aid in their continued linguistic study, and in other areas, such as language processing, augmenting dictionaries, improving search engines, analyzing the construction of sequences, and more."</p>
Researchers devise an effective new predictive tool for maritime first-responders.
- Predicting the locations of objects and people lost at sea is devilishly difficult.
- MIT and other institutions have developed a new algorithm that identifies floating "traps" that can attract floating craft and people.
- The new TRAPS system has just completed a successful first round of testing.
When the first pieces of Malaysian Air Flight 370 finally turned up in July 2015, they were found on Réunion Island off the eastern coast of Africa in the Indian Ocean, thousands of miles from the best-guess location of where the plane went down. Experts weren't especially surprised at the drift, given the complexities of the ocean.
Finding a missing craft or person at sea in a hurry is a nightmare for first responders, and the math involved in tracking survivors — and debris — is anything but simple, given the sea's ever-changing mix of wind, weather, and wave conditions.
Researchers at MIT, the Swiss Federal Institute of Technology (ETH), the Woods Hole Oceanographic Institution (WHOI), and Virginia Tech recently announced the first successful trials of their new "TRAPS" system, a system they hope will provide faster, more accurate insights into the floating locations of missing objects and people by identifying the watery "traps" into which they're likely to be attracted. The team's TRAPS research is published in the journal Nature Communications.
According to Thomas Peacock, professor of mechanical engineering at MIT, "This new tool we've provided can be run on various models to see where these traps are predicted to be, and thus the most likely locations for a stranded vessel or missing person." He adds that, "This method uses data in a way that it hasn't been used before, so it provides first responders with a new perspective."
A Eulerian approach
Image source: MIT
The TRAPS acronym stands for "TRansient Attracting Profiles." It's an algorithm based on a Eulerian mathematical system developed by lead study author Mattia Serra and corresponding author George Haller of ETH Zurich. It's designed to discover hidden attracting fluidic structures in an onrush of changing data.
The traps the researchers seek are regions of water that temporarily converge and pull in objects or people. "The key thing is," says Peacock, "the traps may not have any signature in the ocean current field. If you do this processing for the traps, they might pop up in very different places from where you're seeing the ocean current projecting where you might go. So you have to do this other level of processing to pull out these structures. They're not immediately visible."
The new algorithm crunches through data representing the most reliable available wave-velocity snapshots at the last-known position of the missing item, and rapidly computes the location nearby traps in which a search is likely to be productive. As velocity data is continually updated, so is TRAPS.
Comparing the new Eulerian algorithm with previous Langrangrian predictive methods, Serra says, "We can think of these 'traps' as moving magnets, attracting a set of coins thrown on a table. The Lagrangian trajectories of coins are very uncertain, yet the strongest Eulerian magnets predict the coin positions over short times."
Image source: MIT
Theory is one thing, and functioning out on the real, maddeningly complex ocean is another. "As with any new theoretical technique, it is important to test how well it works in the real ocean," says Wood Hole's Irina Rypina.
The study authors were pleased — and surprised — at how well TRAPS worked. Haller says, "We were a bit skeptical whether a mathematical theory like this would work out on a ship, in real time. We were all pleasantly surprised to see how well it repeatedly did."
The researchers tested TRAPS off Martha's vineyard in the Atlantic Ocean in 2017 and 2018. WHOI sea-going experts assisted as they attempted to track the trajectories of a range of floating objects — buoys and mannequins among them — set into the water at various locations.
One challenge is that different objects may behave in their own ways in the ocean. "These objects tend to travel differently relative to the ocean because different shapes feel the wind and currents differently," according to Peacock.
"Even so," says Peacock, "the traps are so strongly attracting and robust to uncertainties that they should overcome these differences and pull everything onto them."
In their experiments, the researchers tracked freely floating objects for hours via GPS as a way to verify the TRAPS system's predictions. "With the GPS trackers, we could see where everything was going, in real-time," says Peacock. Watching the objects move via GPS, the researchers, "saw that, in the end, they converged on these [predicted] traps."
The researchers now have sufficient faith in TRAPS that they plan on sharing it soon with the U.S. Coast Guard. Says Peacock:
"People like Coast Guard are constantly running simulations and models of what the ocean currents are doing at any particular time and they're updating them with the best data that inform that model. Using this method, they can have knowledge right now of where the traps currently are, with the data they have available. So if there's an accident in the last hour, they can immediately look and see where the sea traps are. That's important for when there's a limited time window in which they have to respond, in hopes of a successful outcome."