Is This the End of the Middleman?

September 20, 2017

The introduction of the internet began to reduce the need for professional intermediaries back in the 1990s, but that trend has accelerated with today’s AI capabilities. The Korea Times examines the matter in, “AI Invigorates ‘Scissors Economy’.”  The term “scissors economy” harkens back to 1999’s Market Shock by Todd Buchholz, in which that author coined the phrase to describe the shrinking reliance on go-betweens prompted by online technologies.

Some of the businesses that have been affected by these changing circumstances included brick-and-mortar stores, travel agents, stockbrokers, and insurance agents. It should come as no surprise– technologies that give consumers more direct control necessarily abridge nearly any transaction, cutting out professional intercessors. Writer Yoon Sung-won observes:

Expectations are that the phenomenon of the scissors economy will gain more strength as industries expedite introducing AI technologies in actual businesses. For instance, financial institutions such as banks, brokerage houses and insurance companies have started to use AI-based technologies not just to recommend optimal financial products to their clients but also to make decisions such as whom to grant loans to and where to invest. In the process, less and less human intervention is needed. Online shopping malls are also rushing to adopt new type of services, also based on AI technologies. Upon the customers’ agreement, online shopping platform operators collect information on their preferences to recommend products for customers to purchase. Internet and gaming service providers also use AI technologies to analyze their users to understand consumption patterns. Advanced medical institutions such as cancer centers are also tapping into AI technologies. In Korea, multiple hospitals including Gachon University Gil Medical Center have introduced IBM’s Watson AI system to give medical advice.

Yoon cites an “industry source” when noting that not many workers have been directly replaced by AI systems yet, but that it is only a matter of time. We’re also cautioned—maybe those humans-in-the-middle are actually beneficial. What world will we create when we hand as much decision-making to algorithms as possible?

Cynthia Murrell, September 20, 2017

Alexa Gets a Physical Body

September 20, 2017

Alexa did not really get physical robot body, instead, Bionik Laboratories developed an Alexa skill to control their AKRE lower-body exoskeleton.  The news comes from iReviews’s article, “Amazon’s Alexa Can Control An Exoskeleton With Verbal Instructions.”

This is the first time Alexa has ever been connected to an exoskeleton and it could potentially lead to amazing breakthroughs in prosthetics.  Bionik Laboratories developed the exoskeleton to help older people and those with lower body impairments.  Users can activate the exoskeleton through Alexa with simple commands like, “I’m ready to stand” or “I’m ready to walk.”

As the population ages, there will be a higher demand for technology that can help senior citizens move around with more ease.

The ARKE exoskeleton has the potential to help in 100% of all stroke survivors who suffer from lower limb impairment. A portion of wheelchair-bound stroke survivors will be eligible for the exoskeleton. For spinal cord injury patients, Bionik Labs expects to treat 80% of all cases with the ARKE exoskeleton. There is also potential for patients with quadriplegia or incomplete spinal cord injury.

Bionik Laboratories plans to help people regain their mobility and improve their quality of life.  The company is focusing on stroke survivors and other mobile-impaired patients.  Pairing the exoskeleton with Alexa demonstrates the potential home healthcare will have in the future.  It will also feed imaginations as they wonder if the exoskeletons can be programmed not only walk and run but search and kill?  Just a joke, but the potential for aiding impaired people is amazing.

Whitney Grace, September 20, 2015

Drugmaker Merk Partners with Palantir on Data Analysis

July 21, 2017

Pharmaceutical company Merk is working with data-analysis firm Palantir on a project to inform future research, we learn from the piece, “Merk Forges Cancer-Focused Big Data Alliance with Palantir” at pharmaceutical news site PMLive. The project is an effort to remove the bottleneck that currently exists between growing silos of medical data and practical applications of that information. Writer Phil Taylor specifies:

Merck will work with Palantir on cancer therapies in the first instance, with the aim of developing a collaborative data and analytics platform for the drug development processes that will give researchers new understanding of how new medicines work. Palantir contends that many scientists in pharma companies struggle with unstructured data and information silos that ‘reduce creativity and limit researchers’ corrective analyses’. The data analytics and sharing platform will help Merck researchers analyse real-world and bioinformatics data so they can ‘understand the patients who may benefit most’ from a treatment.

The alliance also has a patient-centric component, and according to Merck will improve the experience of patients using its products, improve adherence as well as provide feedback on real-world efficacy.

Finally, the two companies will collaborate on a platform that will allow improved global supply chain forecasting and help to get medicines to patients who need them around the world as quickly as possible. Neither company has disclosed any financial details on the deal.

This is no surprise move for the 125-year-old Merk, which has been embracing digital technology in part by funding projects around the world. Known as MSD everywhere but the U.S. and Canada, the company started with a small pharmacy in Germany but now has its headquarters in New Jersey.

Palantir has recently stirred up some controversy. The company’s massive-scale data platforms allow even the largest organizations to integrate, manage, and secure all sorts of data. Its founding members include PayPal alumni and Stanford computer-science grads. The company is based in Palo Alto, California, and has offices around the world.

Cynthia Murrell, July 21, 2017

Big Data in Biomedical

July 19, 2017

The biomedical field which is replete with unstructured data is all set to take a giant leap towards standardization with Biological Text Mining Unit.

According to PHYS.ORG, in a peer review article titled Researchers Review the State-Of-The-Art Text Mining Technologies for Chemistry, the author states:

Being able to transform unstructured biomedical research data into structured databases that can be more efficiently processed by machines or queried by humans is critical for a range of heterogeneous applications.

Scientific data has fixed set of vocabulary which makes standardization and indexation easy. However, most big names in Big Data and enterprise search are concentrating their efforts on e-commerce.

Hundreds of new compounds are discovered every year. If the data pertaining to these compounds is made available to other researchers, advancements in this field will be very rapid. The major hurdle is the data is in an unstructured format, which Biological Text Mining Unit standards intend to overcome.

Vishal Ingole, July 19, 2017

Study: Social Media and Young People

July 19, 2017

Some of us elders have been saying it for years, but now research seems to confirm it—social media can be bad for mental health.  The Next Web reports, “Study: Snapchat and Instagram Are the Worst for Young People.” The study is from the UK’s Royal Society for Public Health (RSPH), and the “young people” sampled are 1,479 Brits aged 14-24. An explanatory three-minute video from the RSPH accompanies the article. Writer Rachel Kaser reports:

The researchers surveyed 1,479 British youths ages 14-24, asking them how they felt the different social media networks effected their mental health. They took in several factors such as body image, sleep deprivation, bullying, and self-identity. The results suggest the two worst social media networks for kids are Instagram and Snapchat, as they had terrible scores for body image, bullying, and anxiety. Twitter and Facebook weren’t much better, though. YouTube was the only one that apparently inspired more positive feelings than negative ones. It could be because Snapchat and Instagram are image-based apps, meaning it’s not easy for users to avoid visual comparisons. Both apps ranked high on ‘Fear of Missing Out,’ and the researchers suggested this was likely to foster anxiety in fellow users.

I recommend the video for interested readers. It shows some respondents’ answers to certain questions, and clearly summarizes the pros and cons of each platform examined. It helpfully concludes with a list of concrete suggestions: Implement pop-up notifications that tell users when they’ve been online for a certain amount of time; require watermarks on photos that have been digitally altered; educate folks on the healthy use of social media; and incorporate analysis tools to identify users at risk for poor mental health and “discreetly” steer them toward help. It does seem such measures could help; will social-media companies cooperate?

Cynthia Murrell, July 19, 2017

Hope for Improvement in Predictive Modeling

July 18, 2017

A fresh approach to predictive modeling may just improve the process exponentially. Phys.org reports, “Molecular Dynamics, Machine Learning Create ‘Hyper-Predictive Computer Models.” The insight arose, and is being tested, at North Carolina State University.

The article begins by describing the incredibly complex and costly process of drug development, including computer models that predict the effects of certain chemical compounds. Such models traditionally rely on QSAR modeling and molecular docking. We learn:

Denis Fourches, assistant professor of computational chemistry, wanted to improve upon the accuracy of these QSAR models. … Fourches and Jeremy Ash, a graduate student in bioinformatics, decided to incorporate the results of molecular dynamics calculations – all-atom simulations of how a particular compound moves in the binding pocket of a protein – into prediction models based on machine learning. ‘Most models only use the two-dimensional structures of molecules,’ Fourches says. ‘But in reality, chemicals are complex three-dimensional objects that move, vibrate and have dynamic intermolecular interactions with the protein once docked in its binding site. You cannot see that if you just look at the 2-D or 3-D structure of a given molecule.’

See the article for some details about the team’s proof-of-concept study. Fourches asserts the breakthrough delivers a simulation that would previously have been built over six months in a mere three hours. That is quite an improvement! If this technique pans out, we could soon see more rapid prediction not only in pharmaceuticals but many other areas as well. Stay tuned.

Cynthia Murrell, July 18, 2017

Can an Algorithm Tame Misinformation Online?

June 23, 2017

UCLA researchers are working on an algorithmic solution to the “fake news” problem, we learn from the article, “Algorithm Reads Millions of Posts on Parenting Sites in Bid to Understand Online Misinformation” at TechRadar. Okay, it’s actually indexing and text analysis, not “reading,” but we get the idea. Reporter Duncan Geere tells us:

There’s a special logic to the flow of posts on a forum or message board, one that’s easy to parse by someone who’s spent a lot of time on them but kinda hard to understand for those who haven’t. Researchers at UCLA are working on teaching computers to understand these structured narratives within chronological posts on the web, in an attempt to get a better grasp of how humans think and communicate online.

Researchers used the hot topic of vaccinations, as discussed on two parenting forums, as their test case. Through an examination of nearly 2 million posts, the algorithm was able to come to accurate conclusions, or “narrative framework.” Geere writes:

While this study was targeted at conversations around vaccination, the researchers say the same principles could be applied to any topic. Down the line, they hope it could allow for false narratives to be identified as they develop and countered by targeted messaging.

The phrase “down the line” is incredibly vague, but the sooner the better, we say (though we wonder exactly what form this “targeted messaging” will take). The original study can be found here at eHealth publisher JMIR Publications.

Cynthia Murrell, June 23, 2017

 

Academic Publisher Retracts Record Number of Papers

June 20, 2017

To the scourge of fake news we add the problem of fake research. Retraction Watch announces “A New Record: Major Publisher Retracting More Than 100 Studies from Cancer Journal over Fake Peer Reviews.”  We learn that Springer Publishing Company has just retracted 107 papers from a single journal after discovering their peer reviews had been falsified. Faking the integrity of cancer research? That’s pretty low. The article specifies:

To submit a fake review, someone (often the author of a paper) either makes up an outside expert to review the paper, or suggests a real researcher — and in both cases, provides a fake email address that comes back to someone who will invariably give the paper a glowing review. In this case, Springer, the publisher of Tumor Biology through 2016, told us that an investigation produced “clear evidence” the reviews were submitted under the names of real researchers with faked emails. Some of the authors may have used a third-party editing service, which may have supplied the reviews. The journal is now published by SAGE. The retractions follow another sweep by the publisher last year, when Tumor Biology retracted 25 papers for compromised review and other issues, mostly authored by researchers based in Iran.

The article shares Springer’s response to the matter, some from their official statement and some from a spokesperson. For example, we learn the company cut ties with the “Tumor Biology” owners, and that the latest fake reviews were caught during a process put in place after that debacle.  See the story for more details.

Cynthia Murrell, June 20, 2017

Partnership Hopes to Improve Healthcare through Technology

June 5, 2017

A healthcare research organization and a data warehousing and analytics firm are teaming up to improve patient care, Healthcare IT News reports in, “Health Catalyst, Regenstrief Partner to Commercialize Natural Language Processing Technology.” The technology at hand is the nDepth (NLP Data Extraction Providing Targeted Healthcare) platform, Regenstrief’s specialized data analysis tool. Reporter Bernie Monegain elaborates:

Regenstrief’s nDepth is artificial intelligence-powered text analytics technology. It was developed within the Indiana Health Information Exchange, the largest and oldest HIE in the country. Regenstrief fine-tuned nDepth through extensive and repeated use, searching more than 230 million text records from more than 17 million patients. The goal of the partnership is to speed improvements in patient care by unlocking the unstructured data within electronic health records. Health Catalyst will incorporate nDepth into its data analytics platform in use by health systems that together serve 85 million patients across the country.

In addition, clinicians are contributing their knowledge to build and curate clinical domain expertise and phenotype libraries to augment the platform. Another worthy contributor is Memorial Hospital at Gulfport, which was a co-development partner and was the first to implement the Health Catalyst/ nDepth system.

Based in Indianapolis, the Regenstrief Institute was founded in 1969 with a mission—to facilitate the use of technology to improve patient care. Launched in 2008, Health Catalyst is much younger but holds a similar purpose—to improve healthcare with data analysis and information sharing technologies. That enterprise is based in Salt Lake City.

Cynthia Murrell, June 5, 2017

Linguistic Analytics Translate Doctor Scribbles

May 31, 2017

Healthcare is one of the industries that people imagine can be revolutionized by new technology.  Digital electronic medical records, faster, more accurate diagnostic tools, and doctors having the ability to digest piles of data in minutes are some of the newest and best advances in medicine.  Despite all of these wonderful improvements, healthcare still lags behind other fields transforming their big data into actionable, usable data.  Inside Big Data shares the article, “How NLP Can Help Healthcare ‘Catchup’” discusses how natural language processing can help the healthcare industry make more effective use of their resources.

The reason healthcare lags behind other fields is that most of their data is unstructured:

This large realm of unstructured data includes qualitative information that contributes indispensable context in many different reports in the EHR, such as outside lab results, radiology images, pathology reports, patient feedback and other clinical reports. When combined with claims data this mix of data provides the raw material for healthcare payers and health systems to perform analytics. Outside the clinical setting, patient-reported outcomes can be hugely valuable, especially for life science companies seeking to understand the long-term efficacy and safety of therapeutic products across a wide population.

Natural language processing relies on linguistic algorithms to identify key meanings in unstructured data.  When meaning is given to unstructured data, then it can be inserted into machine learning algorithms.  Bitext’s computational linguistics platform does the same with its sentimental analysis algorithm. Healthcare information is never black and white like data in other industries.  While the unstructured data is different from patient to patient, there are similarities and NLP helps the machine learning tools learn how to quantify what was once-unquantifiable.

Whitney Grace, May 31, 2017

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