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

Does This Count As Irony?

May 16, 2017

Does this count as irony?

Palantir, who has built its data-analysis business largely on its relationships with government organizations, has a Department of Labor analysis to thank for recent charges of discrimination. No word on whether that Department used Palantir software to “sift through” the reports. Now, Business Insider tells us, “Palantir Will Shell Out $1.7 Million to Settle Claims that It Discriminated Against Asian Engineers.” Writer Julie Bort tells us that, in addition to that payout, Palantir will make job offers to eight unspecified Asians. She also explains:

The issue arose because, as a government contractor, Palantir must report its diversity statistics to the government. The Labor Department sifted through these reports and concluded that even though Palantir received a huge number of qualified Asian applicants for certain roles, it was hiring only small numbers of them. Palantir, being the big data company that it is, did its own sifting and produced a data-filled response that it said refuted the allegations and showed that in some tech titles 25%-38% of its employees were Asians. Apparently, Palantirs protestations weren’t enough on to satisfy government regulators, so the company agreed to settle.

For its part, Palantir insists on their innocence but say they settled in order to put the matter behind them. Bort notes the unusual nature of this case—according to the Equal Employment Opportunity Commission, African-Americans, Latin-Americans, and women are more underrepresented in tech fields than Asians. Is the Department of Labor making it a rule to analyze the hiring patterns of companies required to report diversity statistics? If they are consistent, there should soon be a number of such lawsuits regarding discrimination against other groups. We shall see.

Cynthia Murrell, May 16, 2017

Medical Records Are the Hot New Dark Web Commodity

January 10, 2017

From emails to Netflix and Uber account information to other personally identifiable information has long been for sale on the Dark Web. A recent article from Fast Company, On The Dark Web, Medical Records Are A Hot Commodity, shares that medical records are the latest offerings for sale on the Dark Web. Medical records sold in these marketplaces usually include an individual’s name, birthdate, social security number and medical information. They fetch the relatively high price of $60 a piece, in comparison to social security numbers at $15. The article explains more,

On the dark web, medical records draw a far higher price than credit cards. Hackers are well aware that it’s simple enough to cancel a credit card, but to change a social security number is no easy feat. Banks have taken some major steps to crack down on identity theft. But hospitals, which have only transitioned en masse from paper-based to digital systems in the past decade, have far fewer security protections in place.

Cybercrime of medical records is potentially life-threatening because oftentimes during the theft of medical records, data showing allergies and other vital information is erased or swapped. Hopefully, the amount of time it took the medical industry to transition from paper to electronic health records is not representative of the time it will take the industry to increase security measures.

Megan Feil, January 10, 2017

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