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Discoverys data science team has been blazing new trails in the payment integrity industry through advanced analytics and machine learning capabilities.

ITASCA, Ill. (PRWEB) December 16, 2019

Discovery Health Partners has been named an honoree of a 2020 FutureEdge 50 Award from IDGs CIO. This prestigious honor is given to organizations that are pushing the edge with new technologies to advance their business for the future. Discovery will accept its award at the AGENDA20 Conference held March 23 25 at the Sawgrass Marriott Golf Resort & Spa in Ponte Vedra Beach, Florida.

Discoverys Case Open Logic initiative was piloted earlier this year to test machine learning as part of its claims ranking process within its Subrogation practice. Already a leader in analytics-powered technology solutions for some of the top health payers in the country, Discovery saw an opportunity to leverage its decade of case outcomes data to fuel a subrogation model that eliminates hours of manual work, results in more accurate case identification, and reduces member friction. Discovery is implementing improvements and enhancements and intends to roll out similar machine learning capabilities to other lines of business such as Data Mining, Clinical Audit and Coordination of Benefits (COB).

Discoverys data science team has been blazing new trails in the payment integrity industry through advanced analytics and machine learning capabilities, stated Discovery CIO Dan Iantorno. Combining technology innovation with our teams decades of experience in the health payer space is at the heart of how we deliver game-changing results for our customers.

The organizations in the FutureEdge 50 are doing exciting things that would have been unimaginable just a few years ago. For example, theres a project using blockchain and quantum computing to establish trust; an indoor navigation system; and many uses of AI/ML for insights and efficiency, said Anne McCrory, group vice president, customer experience and operations, events, and the FutureEdge and AGENDA20 conference chair. We are honored to showcase these innovations and many others as we enter into a new era of sophistication with cloud, devices, and environments powering the technology-driven business.

For more information about how Discovery Health Partners award-winning team and technologies are helping health payers solve payment integrity challenges to improve financial and member outcomes, visit https://www.discoveryhealthpartners.com/.

About Discovery Health PartnersDiscovery Health Partners mission is to deliver unique, actionable analytic insights and technology-powered solutions to help healthcare payers improve payment integrity, increase revenue optimization, and maximize efficiencies with government programs. Serving more than 70 health plans across the U.S., including six of the 10 largest U.S. health plans, Discovery has been recognized consistently for its growthearning a spot on the Inc. 5000 list six years in a row and the Crains Fast 50 three times. For more information on Discovery Health Partners, go to http://www.DiscoveryHealthPartners.com.

About the FutureEdge 50 Awards The FutureEdge 50 awards recognize organizations pushing the edge with new technologies to advance their business for the future. The successor to the Digital Edge 50 awards, the FutureEdge 50 will recognize not only established initiatives driving business success but also early-stage projects pursued for their watershed potential. These initiatives may be in R&D, proof of concept or pilot phases. With this, the FutureEdge 50 awards aim to bring the most cutting-edge trials and applications of emerging technologies and the innovative cultures enabling them to our audience at the AGENDA conference.

About CIO CIO focuses on attracting the highest concentration of enterprise CIOs and business technology executives with unparalleled expertise on business strategy, innovation, and leadership. As organizations grow with digital transformation, CIO provides its readers with invaluable peer insights on the evolving CIO role as well as how leading IT organizations are employing technologies, including automation, AI & machine learning, data analytics and cloud, to create business value.

The award-winning CIO portfolio CIO.com, CIO events, CIO Strategic Marketing Services, CIO Forum on LinkedIn, CIO Executive Council and CIO primary research provides business technology leaders with analysis and insight on information technology trends and a keen understanding of ITs role in achieving business goals. CIO is published by IDG Communications, Inc. Company information is available at http://www.idg.com.

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Discovery Health Partners' Case Open Logic Machine Learning Initiative Receives FutureEdge 50 Award from IDG - PR Web

When one technology replaces another, its not easy to accurately ascertain how the new technology would impact our lives. With so much buzz around the modern applications of Artificial Intelligence, Machine Learning, and Data Science, it becomes difficult to track the developments of these technologies. Machine Learning, in particular, has undergone a remarkable evolution in recent years. Many Machine Learning (ML) techniques have come in the foreground recently, most of which go beyond the traditionally simple classifications of this highly scientific Data Science specialization.

Read More: Beyond RPA And Cognitive Document Automation: Intelligent Automation At Scale

Lets point out the top ML techniques that the industry leaders and investors are keenly following, their definition, and commercial application.

Perceptual Learning is the scientific technique of enabling AI ML algorithms with better perception abilities to categorize and differentiate spatial and temporal patterns in the physical world.

For humans, Perceptual Learning is mostly instinctive and condition-driven. It means humans learn perceptual skills without actual awareness. In the case of machines, these learning skills are mapped implicitly using sensors, mechanoreceptors, and connected intelligent machines.

Most AI ML engineering companies boast of developing and delivering AI ML models that run on an automated platform. They openly challenge the presence and need for a Data Scientist in the Engineering process.

Automated Machine Learning (AutoML) is defined as the fully automating the entire process of Machine Learning model development right up till the process of its application.

AutoML enables companies to leverage AI ML models in an automated environment without truly seeking the involvement and supervision of Data Scientists, AI Engineers or Analysts.

Google, Baidu, IBM, Amazon, H2O, and a bunch of other technology-innovation companies already offer a host of AutoML environment for many commercial applications. These applications have swept into every possible business in every industry, including in Healthcare, Manufacturing, FinTech, Marketing and Sales, Retail, Sports and more.

Bayesian Machine Learning is a unique specialization within AI ML projects that leverage statistical models along with Data Science techniques. Any ML technique that uses the Bayes Theorem and Bayesian statistical modeling approach in Machine Learning fall under the purview of Bayesian Machine Learning.

The contemporary applications of Bayesian ML involves the use of open-source coding platform Python. Unique applications include

A good ML program would be expected to perpetually learn to perform a set of complex tasks. This learning mechanism is understood from the specialized branch of AI ML techniques, called Meta-Learning.

The industry-wide definition for Meta-Learning is the ability to learn and generalize AI into different real-world scenarios encountered during the ML training time, using specific volume and variety of data.

Meta-Learning techniques can be further differentiated into three categories

In each of these categories, there is a unique learner, meta-learner, and vectors with labels that match Data-Time-Spatial vectors into a set of networking processes to weigh real-world scenarios labeled with context and inferences.

All the recent Image Processing and Voice Search techniques use the Meta-Learning techniques for their outcomes.

Adversarial ML is one of the fastest-growing and most sophisticated of all ML techniques. It is defined as the ML technique adopted to test and validate the effectiveness of any Machine Learning program in an adverse situation.

As the name suggests, its the antagonistic principle of genuine AI, but used nonetheless to test the veracity of any ML technique when it encounters a unique, adverse situation. It is mostly used to fool an ML model into doubting its own results, thereby leading to a malfunction.

Most ML models are capable of generating answer for one single parameter. But, can it be used to answer for x (unknown or variable) parameter. Thats where the Causal Inference ML techniques comes into play.

Most AI ML courses online are teaching Causal inference as a core ML modeling technique. Causal inference ML technique is defined as the causal reasoning process to draw a unique conclusion based on the impact variables and conditions have on the outcome. This technique is further categorized into Observational ML and Interventional ML, depending on what is driving the Causal Inference algorithm.

Also commercially popularized as Explainable AI (X AI), this technique involves the use of neural networking and interpretation models to make ML structures more easily understood by humans.

Deep Learning Interpretability is defined as the ML specialization to remove black boxes in AI models, providing decision-makers and data officers to understand data modeling structures and legally permit the use of AI ML for general purposes.

The ML technique may use one or more of these techniques for Deep Learning Interpretation.

Any data can be accurately plotted using graphs. In Machine Learning techniques, a graph is a data structure consisting of two components, Vertices (or nodes) and Edges.

Graph ML networks is a specialized ML technique used to connect problems with edges and graphs. Graph Neural Networks (NNs) give rise to the category of Connected NNs (CNSS) and AI NNs (ANN).

There are at least 50 more ML techniques that could be learned and deployed using various NN models and systems. Click here to know of the leading ML companies that are constantly transforming Data Science applications with AI ML techniques.

(To share your insights about ML techniques and commercial applications, please write to us at info@aithority.com)

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10 Machine Learning Techniques and their Definitions - AiThority

Processing big data in real-time for artificial intelligence, machine learning, and the Internet of Things poses significant infrastructure challenges.

Whether it is for autonomous vehicles, connected devices, or scientific research, legacy NoSQL solutions often struggle at hyperscale. Theyve been built on top of existing RDBMs and tend to strain when looking to analyze and act upon data at hyperscale - petabytes and beyond.

DBTA recently held a webinar featuring Theresa Melvin, chief architect of AI-driven big data solutions, HPE, and Noel Yuhanna, principal analyst serving enterprise architecture professionals, Forrester, who discussed trends in what enterprises are doing to manage big data in real-time.

Data is the new currency and it is driving todays business strategy to fuel innovation and growth, Yuhanna said.

According to a Forrester survey, the top data challenges are data governance, data silos, and data growth, he explained.

More than 35% of enterprises have failed to get value from big data projects largely because of skills, budget, complexity and strategy. Most organizations are dealing with growing multi-format data volume thats in multiple repositories -relational, NoSQL, Hadoop, data lake..

The need has grown for real-time and agile data requirements, he explained. There are too many data silos multiple repositories, cloud sources.

There is a lack of visibility into data across personas -- developer, data scientist, data engineers, data architects, security etc..Traditional data platforms have failed to support new business requirements such as data warehouse, relational DBMS, and ETL tools.

Its all about the customer and its critical for organizations to have a platform to succeed, Yuhanna said. Customers prefer personalization. Companies are still early on their AI journey but they believe it will improve efficiency and effectiveness.

AI and machine learning can hyper-personalize customer experience with targeted offers, he explained. It can also prevent line shutdowns by predicting machine failures.

AI is not one technology. It is comprised of one or more building block technologies. According to the Forrester survey, Yuhanna said AI/ML for data will help end-users and customers to support data intelligence to support new next-generation use cases such as customer personalization, fraud detection, advanced IoT analytics and rea-time data sharing and collaboration.

AI/ML as a platform feature will help support automation within the BI platform for data integration, data quality, security, governance, transformation, etc., minimizing human effort required. This helps deliver insights quicker in hours instead of days and months.

Melvin suggested using HPE Persistent Memory. The platform offers real-time analysis, real-time persist, a single source of truth, and a persistent record.

An archived on-demand replay of this webinar is available here.

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Managing Big Data in Real-Time with AI and Machine Learning - Database Trends and Applications

The NFL and Amazon announced an expansion of their partnership at their annual AWS re:Invent ... [+] conference in Las Vegas that will use artificial intelligence and machine learning to combat player injuries. (Photo by Michael Zagaris/San Francisco 49ers/Getty Images)

Injury prevention in sports is one of the most important issues facing a number of leagues. This is particularly true in the NFL, due to the brutal nature of that punishing sport, which leaves many players sidelined at some point during the season. A number of startups are utilizing technology to address football injury issues, specifically limiting the incidence of concussions. Now, one of the largest companies in the world is working with the league in these efforts.

A week after partnering with the Seattle Seahawks on its machine learning/artificial intelligence offerings, Amazon announced a partnership Thursday in which the technology giant will use those same tools to combat football injuries. Amazon has been involved with the league, with its Next Gen Stats partnership, and now the two companies will work to advance player health and safety as the sport moves forward after its 100th season this year. Amazons AWS cloud services will use its software to gather and analyze large volumes of player health data and scan video images with the objective of helping teams treat injuries and rehabilitate players more effectively. The larger goal will be to create a new Digital Athlete platform to anticipate injury before it even takes place.

This partnership expands the quickly growing relationship between the NFL and Amazon/AWS. as the two have already teamed up for two years with the leagues Thursday Night Football games streamed on the companys Amazon Prime Video platform. Amazon paid $130 million for rights that run through next season. The league also uses AWSs ML Solutions Lab,as well as Amazons SageMaker platform, that enables data scientists and developers to build and develop machine learning models that can also lead to the leagues ultimate goal of predicting and limiting player injury.

The NFL is committed to re-imagining the future of football, said NFL Commissioner Roger Goodell. When we apply next-generation technology to advance player health and safety, everyone wins from players to clubs to fans. The outcomes of our collaboration with AWS and what we will learn about the human body and how injuries happen could reach far beyond football. As we look ahead to our next 100 seasons, were proud to partner with AWS in that endeavor.

The new initiative was announced as part of Amazons AWS re:Invent conference in Las Vegas on Thursday. Among the technologies that AWS and the league announced in its Digital Athlete platform is a computer-simulated model of an NFL player that will model infinite scenarios within NFL gameplay in order to identify a game environment that limits the risk to a player. Digital Athlete uses Amazons full arsenal of technologies, including the AI, ML and computer vision technology that is used with Amazons Rekognition tool and that uses enormous data sets encompassing historical and more modern video to identify a wide variety of solutions, including the prediction of player injury.

By leveraging the breadth and depth of AWS services, the NFL is growing its leadership position in driving innovation and improvements in health and player safety, which is good news not only for NFL players but also for athletes everywhere, said Andy Jassy, CEO of AWS. This partnership represents an opportunity for the NFL and AWS to develop new approaches and advanced tools to prevent injury, both in and potentially beyond football.

These announcements come at a time when more NFL players are utilizing their large platforms to bring awareness to injuries and the enormous impact those injuries have on their bodies. Former New England Patriots tight end Rob Gronkowski has been one of the most productive NFL players at his position in league history but he had to retire from the league this year, at the age of 29, due to a rash of injuries.

The future Hall of Fame player estimated that he suffered probably 20 concussions in his football career. These admissions have significant consequences on youth participation rates in the sport. Partnerships like the one announced yesterday will need to be successful in order for the sport to remain on solid footing heading into the new decade.

Read the rest here:

The NFL And Amazon Want To Transform Player Health Through Machine Learning - Forbes

Jen-Hsun Huang, president and chief executive officer of Nvidia Corp., gestures as he speaks during ... [+] the company's event at the 2019 Consumer Electronics Show (CES) in Las Vegas, Nevada, U.S., on Sunday, Jan. 6, 2019. CES showcases more than 4,500 exhibiting companies, including manufacturers, developers and suppliers of consumer technology hardware, content, technology delivery systems and more. Photographer: David Paul Morris/Bloomberg

We found that if Nvidia Stock drops 10% or more in a week (5 trading days), there is a solid 36% chance itll recover 10% or more, over the next month (about 20 trading days)

Nvidia stock has seen significant volatility this year. While the company has been impacted by the broader correction in the semiconductor space and the trade war between the U.S. and China, the stock is being supported by a strong long-term outlook for GPU demand amid growing applications in Deep Learning and Artificial Intelligence.

Considering the recent price swings, we started with a simple question that investors could be asking about Nvidia stock: given a certain drop or rise, say a 10% drop in a week, what should we expect for the next week? Is it very likely that the stock will recover the next week? What about the next month or a quarter? You can test a variety of scenarios on the Trefis Machine Learning Engine to calculate if Nvidia stock dropped, whats the chance itll rise.

For example, after a 5% drop over a week (5 trading days), the Trefis machine learning engine says chances of an additional 5% drop over the next month, are about 40%. Quite significant, and helpful to know for someone trying to recover from a loss. Knowing what to expect for almost any scenario is powerful. It can help you avoid rash moves. Given the recent volatility in the market, the mix of macroeconomic events (including the trade war with China and interest rate easing by the U.S. Fed), we think investors can prepare better.

Below, we also discuss a few scenarios and answer common investor questions:

Question 1: Does a rise in Nvidia stock become more likely after a drop?

Answer:

Not really.

Specifically, chances of a 5% rise in Nvidia stock over the next month:

= 40%% after Nvidia stock drops by 5% in a week.

versus,

= 44.5% after Nvidia stock rises by 5% in a week.

Question 2: What about the other way around, does a drop in Nvidia stock become more likely after a rise?

Answer:

No.

Specifically, chances of a 5% decline in Nvidia stock over the next month:

= 40% after NVIDIA stock drops by 5% in a week

versus,

= 27% after NVIDIA stock rises by 5% in a week

Question 3: Does patience pay?

Answer:

According to data and Trefis machine learning engines calculations, largely yes!

Given a drop of 5% in Nvidia stock over a week (5 trading days), while there is only about 28% chance the Nvidia stock will gain 5% over the subsequent week, there is more than 58% chance this will happen in 6 months.

The table below shows the trend:

Trefis

Question 4: What about the possibility of a drop after a rise if you wait for a while?

Answer:

After seeing a rise of 5% over 5 days, the chances of a 5% drop in Nvidia stock are about 30% over the subsequent quarter of waiting (60 trading days). However, this chance drops slightly to about 29% when the waiting period is a year (250 trading days).

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams|Product, R&D, and Marketing Teams More Trefis Data Like our charts? Exploreexample interactive dashboardsand create your own

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Machine Learning Answers: If Nvidia Stock Drops 10% A Week, Whats The Chance Itll Recoup Its Losses In A Month? - Forbes