Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
From 260-year-old ciphers to the most recent Zodiac Killer solution, these unbreakable codes just needed time.
- After 51 years, the Zodiac Killer's infamous "340 code" has been solved.
- Humans have a natural passion for puzzles, making cryptography a lifelong pursuit for some.
- Other famous cracked codes include Poe's Challenge and Zimmermann's Letter.
How I cracked the Zodiac Killer's cipher<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="d0b8a7170b77210c07cfad50b99ef328"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/3sLFRm29eto?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><h2>Zodiac Killer</h2><p>After the Zodiac Killer's first cryptogram was quickly solved in 1969, he followed up with a 340-character puzzle that has baffled cryptographers ever since. Three men worked tirelessly on the letter and <a href="https://www.wired.com/story/zodiac-killers-cipher-finally-cracked-after-51-years/" target="_blank" rel="noopener noreferrer">finally revealed the encoded message</a>: </p><p>I HOPE YOU ARE HAVING LOTS OF FUN IN TRYING TO CATCH ME THAT WASN'T ME ON THE TV SHOW WHICH BRINGS UP A POINT ABOUT ME I AM NOT AFRAID OF THE GAS CHAMBER BECAUSE IT WILL SEND ME TO PARADICE ALL THE SOONER BECAUSE I NOW HAVE ENOUGH SLAVES TO WORK FOR ME WHERE EVERYONE ELSE HAS NOTHING WHEN THEY REACH PARADICE SO THEY ARE AFRAID OF DEATH I AM NOT AFRAID BECAUSE I KNOW THAT MY NEW LIFE WILL BE AN EASY ONE IN PARADICE DEATH</p><p>While the San Francisco branch of the FBI has acknowledged the puzzle has been solved, they're not providing any more comments considering the case remains open. </p><h2>Poe's Challenge </h2><p>Edgar Allan Poe's "The Gold Bug" was based on a cipher mystery, as Poe himself was fascinated with puzzles. In 1840, he offered a free subscription to Graham's Magazine to anyone who could stump him. He claims to have solved a hundred entries, ending the contest by publishing a challenging code written by W.B. Tyler—who many at the time suspected was a pseudonym.</p><p>It wasn't until 2000 that a <a href="https://www.scientificamerican.com/article/a-cipher-from-poe-solved/" target="_blank" rel="noopener noreferrer">software engineer decoded the message</a>, which opened up, "It was early spring, warm and sultry glowed the afternoon. The very breezes seemed to share the delicious languor of universal nature..."</p><p>Given the numerous typesetting mistakes, recent researchers aren't convinced that Poe actually wrote it. The author will likely remain a mystery, but the code itself is in the books. </p><h2>Copiale cipher</h2><p>An entire team spanning two countries was needed to crack the 260-year-old mystery of the <a href="https://cl.lingfil.uu.se/~bea/copiale/" target="_blank" rel="noopener noreferrer">Copiale cipher</a>. Unlike a few lines of prose, this 75,000-character manuscript filled 105 pages written by a group of ophthalmologists. The book was encrypted in German and relied on a complex substitution code that used symbols and letters for spaces as well as text. </p><p>Dating from the second half of the eighteenth century, the first 16 pages discuss a masonic initiation ceremony by the Oculists. The strange ritual involves initiates "reading" a blank piece of paper before being given a pair of glasses—those wily eye doctors. After their eyes are washed, the referees then pluck a single eyebrow of each recruit. </p><p>Better than college hazing, though still an odd text to keep so secretive. Then again, maybe that was the point. </p>
Slate statue of Mathematician Alan Turing at Bletchley Park
Credit: lenscap50 / Adobe Stock<h2>The Zimmermann Telegram</h2><p>Not all codes are so playful, or strange. Some are insidious. Such is the case with the <a href="https://www.history.com/news/what-was-the-zimmermann-telegram" target="_blank" rel="noopener noreferrer">Zimmermann Telegram</a>, a note sent from Germany to Mexico in 1917. Intended for the German ambassador to Mexico, Heinrich von Eckardt, the Germans were preparing America's southern neighbors for battle—in the name of Germany. In exchange for weapons and funding, the Mexicans would reclaim Arizona, New Mexico, and Texas upon victory. </p><p>The cipher was cracked about a month after interception by Britain's "Room 40." The text read, in part:</p><p style="margin-left: 20px;">"We make Mexico a proposal of alliance on the following basis: make war together, make peace together, generous financial support and an understanding on our part that Mexico is to reconquer the lost territory in Texas, New Mexico, and Arizona. The settlement in detail is left to you."</p><p>Tensions between the US and Germany were already high; this message pushed America over the edge. A month later, President Wilson overruled his intention of remaining neutral and entered World War I on the side of the Allies. </p><h2>The Enigma Code </h2><p>One of the most famous cracks in history is certainly the <a href="https://www.iwm.org.uk/history/how-alan-turing-cracked-the-enigma-code" target="_blank" rel="noopener noreferrer">Enigma Code</a>. If the Zimmermann Telegram helped us get into World War I, the second chapter only ended in our favor thanks to Alan Turing's unforgettable machine. </p><p>The Germans were utilizing an enciphering machine to pass messages to its Axis partners. Perhaps learning from past mistakes, they changed the entire cipher system on a daily basis. </p><p>Turing responded with his own machinery: the Bombe, Lorenz, and Universal Turing Machine. Thanks to his inventions, alongside tireless efforts by British cryptologists, the Allied forces exploited procedural flaws and operator mistakes by the Germans. The Enigma Code was cracked, saving countless Allied lives and helping turn the tide of the war. </p><p>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a> and <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank" rel="noopener noreferrer">Facebook</a>. His new book is</em> "<em><a href="https://www.amazon.com/gp/product/B08KRVMP2M?pf_rd_r=MDJW43337675SZ0X00FH&pf_rd_p=edaba0ee-c2fe-4124-9f5d-b31d6b1bfbee" target="_blank" rel="noopener noreferrer">Hero's Dose: The Case For Psychedelics in Ritual and Therapy</a>."</em></p>
A new theory suggests that dreams' illogical logic has an important purpose.
Overfitting<p>The goal of machine learning is to supply an algorithm with a data set, a "training set," in which patterns can be recognized and from which predictions that apply to other unseen data sets can be derived.</p><p>If machine learning learns its training set too well, it merely spits out a prediction that precisely — and uselessly — matches that data instead of underlying patterns within it that could serve as predictions likely to be true of other thus-far unseen data. In such a case, the algorithm describes what the data set <em>is</em> rather than what it <em>means</em>. This is called "overfitting."</p><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDc4NTQ4Ni9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY2NDM4NDk1Mn0.bMHbBbt7Nz0vmmQ8fdBKaO-Ycpme5eOCxbjPLEHq9XQ/img.jpg?width=980" id="5049a" class="rm-shortcode" data-rm-shortcode-id="f9a6823125e01f4d69ce13d1eef84486" data-rm-shortcode-name="rebelmouse-image" data-width="1440" data-height="585" />
The value of noise<p>To keep machine learning from becoming too fixated on the specific data points in the set being analyzed, programmers may introduce extra, unrelated data as noise or corrupted inputs that are less self-similar than the real data being analyzed.</p><p>This noise typically has nothing to do with the project at hand. It's there, metaphorically speaking, to "distract" and even confuse the algorithm, forcing it to step back a bit to a vantage point at which patterns in the data may be more readily perceived and not drawn from the specific details within the data set.</p><p>Unfortunately, overfitting also occurs a lot in the real world as people race to draw conclusions from insufficient data points — xkcd has a fun example of how this can happen with <a href="https://xkcd.com/1122/" target="_blank">election "facts."</a></p><p>(In machine learning, there's also "underfitting," where an algorithm is too simple to track enough aspects of the data set to glean its patterns.)</p><img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDc4NTQ5My9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMDE5NjY1M30.iS2bq7WEQLeS34zNFPnXwzAZZn9blCyI-KVuXmcHI6o/img.jpg?width=980" id="cd486" class="rm-shortcode" data-rm-shortcode-id="c49cfbbffceb00e3f37f00e0fef859d9" data-rm-shortcode-name="rebelmouse-image" data-width="1440" data-height="810" />
Credit: agsandrew/Adobe Stock
Nightly noise<p>There remains a lot we don't know about how much storage space our noggins contain. However, it's obvious that if the brain remembered absolutely everything we experienced in every detail, that would be an awful lot to remember. So it seems the brain consolidates experiences as we dream. To do this, it must make sense of them. It must have a system for figuring out what's important enough to remember and what's unimportant enough to forget rather than just dumping the whole thing into our long-term memory.</p><p>Performing such a wholesale dump would be an awful lot like overfitting: simply documenting what we've experienced without sorting through it to ascertain its meaning.</p><p>This is where the new theory, the <a href="https://arxiv.org/pdf/2007.09560.pdf" target="_blank">Overfitting Brain Hypothesis</a> (OBH) proposed by Erik Hoel of Tufts University, comes in. Suggesting that perhaps the brain's sleeping analysis of experiences is akin to machine learning, he proposes that the illogical narratives in dreams are the biological equivalent of the noise programmers inject into algorithms to keep them from overfitting their data. He says that this may supply just enough off-pattern nonsense to force our brains to see the forest and not the trees in our daily data, our experiences.</p><p>Our experiences, of course, are delivered to us as sensory input, so Hoel suggests that dreams are sensory-input noise, biologically-realistic noise injection with a narrative twist:</p><p style="margin-left: 20px;">"Specifically, there is good evidence that dreams are based on the stochastic percolation of signals through the hierarchical structure of the cortex, activating the default-mode network. Note that there is growing evidence that most of these signals originate in a top-down manner, meaning that the 'corrupted inputs' will bear statistical similarities to the models and representations of the brain. In other words, they are derived from a stochastic exploration of the hierarchical structure of the brain. This leads to the kind structured hallucinations that are common during dreams."</p><p>Put plainly, our dreams are just realistic enough to engross us and carry us along, but are just different enough from our experiences —our "training set" — to effectively serve as noise.</p><p>It's an interesting theory.</p><p>Obviously, we don't know the extent to which our biological mental process actually resemble the comparatively simpler, man-made machine learning. Still, the OBH is worth thinking about, maybe at least more worth thinking about than whatever <em>that</em> was last night.</p>
The Matrix is already here: Social media promised to connect us, but left us isolated, scared and tribal
The more you like, follow and share, the faster you find yourself moving in that political direction.
This week, Big Think is partnering with Freethink to bring you amazing stories of the people and technologies that are shaping our future.
- There are over 250,000 unsolved murder cases in the United States. Thomas Hargrove, cofounder of Murder Accountability Project, wants that number to be as close to zero as possible, and he has just the tool to help.
- Hargrove developed an algorithm that, through cluster analysis, is capable of finding connections in murder data that human investigators tend to miss.
- The technology exists, but a considerable roadblock that the project faces is getting support and cooperation from law enforcement offices.