Mediaboss Marketing

With supply chain challenges and the ongoing global pandemic regularly introducing new obstacles, sales and marketing professionals must continue to move at the speed of business regardless of where they currently work. According to McKinsey, our new way of working during the pandemic inspired ten years of digital innovation in three months. To ensure no opportunities are missed in this rapidly changing landscape, sales and marketing teams need data to unearth new, actionable insights that they can use to identify in-market prospects and customers at scale.

Over three-fourths of CEOs say that marketing leaders are the key to driving future growth. But they cant do it alone technology will be an essential component fostering that progression. While creative relationship-building and out-of-the-box thinking remain, sales and marketing professionals rely on the latest technology now more than ever to perform their very best. Through technology, marketers and salespeople can now more accurately pinpoint who is genuinely interested in buying their products and services before making contact, which is a powerful, data-driven upgrade over the old model of guesswork and assumptions.

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For example, conferences have traditionally been a way of life for many sales and marketing professionals, providing an opportunity to meet, mingle and network with potential clients. This has obviously changed in the current climate, with far fewer events being scheduled due to Covid-19 concerns. But with the right tools and insights, this is no longer an issue. Sales and marketing execs can take another path instead of spending thousands of dollars attending conferences and engage in relevant ways with both who is expected to attend versus those who actually show up.

Data is an integral part of any sales and marketing strategy. However, data is, after all, just information and simply knowing that a needle is hiding in a haystack is not enough to ensure it is actually found. According to a report by IDC, businesses use less than one-third (32%) of the data available to them. Businesses need great tools to actually put that data to use.

No individual sales or marketing professional can do it alone, and it would be extremely costly for an entire team to invest their working hours in manual lead generation. While this may have once been the only way to accomplish the task, manual work is slow, inefficient and takes valuable resources away from other objectives. Artificial intelligence (AI) and machine learning (ML, a subset of AI) offer a way forward, providing sales and marketing leaders with the power to dig deeper, uncover new information, and gain invaluable market insights. But they can only cut through the clutter of data and differentiate between leads by relying on technology capable of automating the process.

Businesses can instantly gain a competitive edge by deploying technology that relies on both AI and ML models to advance sales and marketing initiatives. They then can act on invaluable insights into what people have been looking at, such as targeted advertising or a thought leadership article and deliver superior results. This results in a higher chance of converting the lead. And with automation in tow, marketers can automatically follow up with targets that have yet to respond within a set amount of time.

As the people tasked with driving future growth, sales leaders need tools that make data simple to use and understand technology that allows them to generate real value from the available information and prioritize their time toward the prospect they can reach and who are more likely to be in-market for their solutions. According to our most recent research, sales professionals say that AI technologies are essential to their day-to-day success, with 70% of sales reps who use AI sales tools saying theyre unsure whether they could meet quotas without them. Data also exists today with leading providers to help sales understand communication preferences and actual engagement, including whether prospects recently answered outbound sales calls or responded to emails.

With AI use on the rise among businesses worldwide, it has quickly become an indispensable tool for sales teams seeking to boost quality lead volume, conversions, and revenue. In todays noisy digital marketplace with each business vying for a bigger piece of the pie, businesses need the power of artificial intelligence and machine learning to ensure they are targeting the right leads every time. With this technology in hand, they can succeed whether working on-site or from home.

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AI and Machine Learning Are the Key to Accelerating Sales and Marketing - MarTech Series

Research and innovation in machine learning in the medical sector has declined in the last year.

The most recent figures show that the number of related patent applications in the industry stood at 78 in the three months ending December down from 156 over the same period in 2020.

Figures for patent grants related to followed a similar pattern to filings shrinking from 27 in the three months ending December 2020 to 14 in the same period in 2021.

The figures are compiled by GlobalData, which tracks patent filings and grants from official offices around the world. Using textual analysis, as well as official patent classifications, these patents are grouped into key thematic areas, and linked to key companies across various industries.

Machine learning is one of the key areas tracked by GlobalData. It has been identified as being a key disruptive force facing companies in the coming years, and is one of the areas that companies investing resources in now are expected to reap rewards from.

The figures also provide an insight into the largest innovators in the sector.

F. Hoffmann-La Roche Ltd was the top innovator in the medical sector in the latest quarter. The company, which has its headquarters in Switzerland, filed 33 related patents in the three months ending December. That was down from 51 over the same period in 2020.

It was followed by the United States based Johnson & Johnson with 30 patent applications, the United Kingdom based Smith & Nephew Plc (12 applications), and Ireland based Medtronic Plc (11 applications).

Johnson & Johnson has recently ramped up R&D in . It saw growth of 30% in related patent applications in the three months ending December compared to the same period in 2020 - the highest percentage growth out of all companies tracked with more than 10 quarterly patents in the medical sector.

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Machine learning innovation among medical industry companies has dropped off in the last year - Medical Device Network

PRINCETON, N.J.--(BUSINESS WIRE)-- ArrePath, an anti-infective drug discovery company addressing the global health challenge of drug resistant infections, announced today that it has raised $20 million in seed financing to advance its proprietary, machine learning (ML)-based platform for the discovery of new classes of anti-infectives to overcome antimicrobial resistance (AMR). The Boehringer Ingelheim Venture Fund, Insight Partners, and Innospark Ventures co-led the financing, which also included Viva BioInnovator, Arimed Capital, PTX Capital, and Noreaster Ventures.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220303005116/en/

Dr. Lloyd Payne, ArrePath President and CEO (Photo: Business Wire)

ArrePath also announced that Dr. Lloyd Payne has been named President and CEO. Dr. Payne, who serves on the Novo REPAIR Impact Fund Scientific Selection Board and the AMR Action Fund Scientific Advisory Board, brings more than 25 years of scientific and business leadership in the discovery and development of anti-infectives. Prior to joining ArrePath, Dr. Payne served at Evotec, as Executive Vice President, Head of Anti-infectives, where he led the companys anti-infective discovery and translational microbiology businesses. Previously, he founded Euprotec, a life sciences company focused on anti-infective drug discovery and development and served as its CEO until its acquisition by Evotec.

ArrePath is an anti-infective drug discovery company addressing the global health challenge of drug resistant infections, which result in at least 1.27 million deaths, annually (according to recent analysis of data from 2019 published in The Lancet, The GRAM Report). New classes of antibiotics that more effectively treat drug resistant infections and overcome antimicrobial resistance are urgently needed. ArrePaths approach leverages a novel technology platform to enable the discovery and development of new and differentiated antibiotics by decoding the complex behavior of bacteria when exposed to new chemical entities (known as bacterial autopsies), leading to the efficient elucidation of biological mechanisms of action. Utilizing proprietary machine learning and imaging technologies, ArrePaths platform enables an unprecedented ability to identify, optimize, and rapidly develop new classes of anti-infectives with differentiated mechanisms of action compared to those exhibited by existing drugs.

The Boehringer Ingelheim Venture Fund is proud to cofound ArrePath with Dr. Zemer Gitai and co-lead the seed series investment to accelerate development of the companys potentially transformative Artificial Intelligence / Machine Learning-based drug discovery platform. Additionally, we are delighted that Dr. Payne, a highly experienced executive and leader in anti-infective drug discovery and development, has joined ArrePath as President and CEO, said Fei Shen, Ph.D., Managing Director, Boehringer Ingelheim Venture Fund USA. Antimicrobial resistance is an area with critical unmet medical need and is one of the Boehringer Ingelheim Venture Funds investment focuses globally. We are committed to playing a key role in the global ecosystem to solve the commercial challenges the area faces and supporting the next generation of anti-infective medicines, added Dr. Shen.

I am delighted to join ArrePath and work with such a talented team to address the central challenge of antimicrobial resistance and further develop the companys innovative platform technology, said Dr. Lloyd Payne, ArrePaths President and CEO. This financing is a strong vote of confidence, by a global investment syndicate, in our platform and its enormous potential in anti-infective drug discovery. The funding will enable the advancement of our initial leads and expansion of our discovery efforts, as well as the enhancement of our imaging platform and the application of machine learning in the discovery of new drugs to address critical global health challenges.

Dr. Gitai, Edwin Grant Conklin Professor of Biology at Princeton University and colleagues published proof-of-concept for the approach in the journal Cell in June 2020. The research describes the identification of a compound with a novel dual mechanism of action against both Gram-negative and Gram-positive bacteria and the platform has since identified additional compounds with novel mechanisms. A Princeton University spin-out, the company has an exclusive option from the university to license intellectual property related to the platform technology, and novel compounds generated through its application.

The worldwide clinical need for new antibiotics that overcome antimicrobial resistance is significant. Analysis by the US Centers for Disease Control and Prevention (CDC) has shown that, in the U.S. alone, drug resistant infections result in at least 35,000 deaths annually. It has been estimated that patients spend an aggregate of eight million additional days in the hospital due to drug resistant infections and cost the U.S. healthcare system between $21 billion and $34 billion. A report by the World Health Organization previously estimated that 750,000 people die each year from resistant infections worldwide but this number has recently been updated. The Lancet report (The GRAM Report), published online, in January 2022, analyzed the global burden of bacterial antimicrobial resistance in 2019 and found at least 1.27 million deaths per year are directly attributable to AMR. The UK-commissioned ONeill Review estimates that unless action is taken, the burden of deaths from AMR could balloon to 10 million lives each year by 2050, at a cumulative cost to global economic output of 100 trillion USD.

About Boehringer Ingelheim Venture Fund

Created in 2010, the Boehringer Ingelheim Venture Fund (BIVF) invests in ground-breaking therapeutics-focused biotechnology companies to drive innovation in biomedical research. BIVF is searching for significant enhancements in patient care through pioneering science and its clinical translation by building long-term relationships with scientists and entrepreneurs. BIVFs focus is to target unprecedented therapeutic concepts addressing high medical needs in immuno-oncology, regenerative medicine, infectious diseases, and digital health. These may include novel platform technologies to address so far undruggable targets, new generation vaccines and/or new biological entities. BIVF takes an active role with its portfolio companies delivering significant added value through its own extensive drug discovery, scientific and managerial expertise. BIVF has a fund volume of 300 million euros and currently supervises a portfolio of more than 40 companies. For more information, please visit http://www.boehringer-ingelheim-venture.com

About Insight Partners

Insight Partners is a leading global venture capital and private equity firm investing in high-growth technology and software ScaleUp companies that are driving transformative change in their industries. Founded in 1995, Insight Partners has invested in more than 400 companies worldwide and has raised through a series of funds more than $30 billion in capital commitments. Insight's mission is to find, fund, and work successfully with visionary executives, providing them with practical, hands-on software expertise to foster long-term success. Across its people and its portfolio, Insight encourages a culture around a belief that ScaleUp companies and growth create opportunity for all. For more information on Insight and all its investments, visit http://www.insightpartners.com or follow us on Twitter @insightpartners.

About Innospark Ventures

Innospark Ventures is a Boston-based early-stage venture fund investing in the AI-powered economy. We believe that computational intelligence (AI) will play an outsized role in the years to come, impacting businesses and industries alike. With decades of entrepreneurial experience across several AI startups, and deep investment expertise across stages, we are thoughtful in our approach to investing. We like foundational, deeply impactful ideas. We view our investments as the beginning of a partnership versus the culmination of a diligence process. Our unique no-LP model allows for patient, founder-friendly, capital. Since the fall of 2018, Innospark has made 20+ investments across the healthcare, life sciences, B2B enterprise, cybersecurity, and education sectors. We are excited to partner with the next generation of founders and can be reached at: info@innosparkventures.com

About ArrePath

ArrePath is an anti-infective drug discovery company addressing the global health challenge of drug resistant infections. The companys proprietary machine learning (ML)-based platform enables the rapid and efficient identification of anti-infective agents with new mechanisms of action at the outset of the discovery process, through a deep understanding and analysis of pathogen behavior. The platforms proof-of-concept has been demonstrated in studies published in Cell by ArrePaths scientific founder, Zemer Gitai, Ph.D., Edwin Grant Conklin Professor of Biology at Princeton University. The company is funded by the Boehringer Ingelheim Venture Fund, Insight Partners, Innospark Ventures, Viva BioInnovator, Arimed Capital, PTX Capital, and Noreaster Ventures. Learn more at http://www.arrepath.com and follow us on Twitter @ArrePath.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220303005116/en/

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ArrePath Announces $20 Million Seed Financing to Advance its Machine Learning-based Platform for Discovery of Novel Anti-infectives Addressing...

Wipros hiring hackathon sustainability machine learning challenge concluded on February 14, 2022. The hackathon had close to 1,880 participants and 550+ solutions posted on the leaderboard. The top three winners of the hackathon will receive cash prizes worth INR 3.5 lakh.

AIM spoke to the winners to understand their data science journey, winning approach, and their overall experience at MachineHack.

Ranjan is currently working at TheMathCompany as a data scientist. A former Infoscian, he has worked in the areas of data science and analytics in the IT and services/product industry. He is skilled in machine learning, modelling, and visualisation using Python. Ranjan is also proficient in strong onsite client interaction and analysing stakeholders needs.

I love interacting with data and creating models which best suit the business needs, along with participating in different ML hackathons to learn new technology and grow professionally, said Ranjan.

I have been part of MachineHack from its inception. Hackathons like these boost the confidence of any aspiring data scientist and help us to grow more technically proficient in the ML/DS field, as consistency is the key to learn and grow, said Ranjan.

Check out the code here.

Taps Das currently works as a data engineer in TheMathCompany. He got interested in machine learning and deep learning in 2018. I went through different MOOCs like the Andrew Ng ML course and Deep Learning Specialisation course on Coursera, said Das.

Further, he said he spent a significant amount of time learning Python programming basics. He then started picking diverse projects from online sources like Kaggle, HackerEarth, Driven Data, to get proficient.

He also participates in various hackathons to stay ahead of the curve. I was inspired and overwhelmed by the ability of ML algorithms to solve a variety of real-world problems, he added.

Das started with extensive EDA to explore the training dataset, which resulted in a few interesting insights, including

After this, he filtered out all records in both train/test datasets, which satisfied the above conditions. He then used feature engineering for the rest of the records, which generated the below feature types.

I changed the problem statement from time-series forecasting to purely regression problem and trained different tree-based models on the same. Finally, I used a weighted average ensemble of LightGBM, CatBoost and XGBoost models to generate the final predictions. Also, I used the Optuna library for hyperparameters search for the different models, said Das.

Competitive DS is a whole different ball game. The winning solutions of most of these challenges involve techniques that are seldom taught in academia, but are used in many production systems, said Das.

For a while, he has been participating in different hackathons on the MachineHack platform. He said he loves how the platform allows anyone, regardless of background or prior experience, to compete on a level playing field where the only thing that matters is optimising a metric.

Winning solutions from previous hackathons are an invaluable learning resource that I highly encourage aspiring participants to leverage. It is fun to compete with the greatest minds in the area of data science, added Das.

Check out the code here.

AppliedAI workshop at my college was my first step, after which I took some courses at Coursera. After taking Deeplearning.ai in my third year of B.Tech, I got a summer internship, then I got my first job offer, became a Kaggle notebook expert and eventually became a grandmaster. Then I got a full-time position, and now I am working at Karmalife.ai as a data scientist, said Durgaprasad.

In the data processing step, after some experiments, Durgaprasad figured out there was some correction over the years. So he took each year as one fold. hE then trained 10-folds each year as a fold and found that Public LB was calculated on 30 percent of data. This was the multi-label regression problem, said Durgaprasad. Hence, he trained the model on each fold, predicted and saved oofs and test prediction for each fold.

In the feature engineering step, he used sample code for creating rolling and shifting features. In the modelling part, he tried catboost multi-label regression and LSTM without any feature engineering, and achieved good results.

MachineHack hackathons are one of my favourite platforms for learning from others and collaborating with others, said Durgaprasad.

Check out the code here.

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Meet the winners of Wipro's Sustainability Machine Learning Challenge - Analytics India Magazine

SAN MATEO, Calif., March 1, 2022 /PRNewswire/ -- Apixio, Inc., the healthcare analytics company, today announced the launch of its new Apicare AuthAdvisor, which uses machine learning and predictive analytics to automate prior authorization decisions for payers. By leveraging historical decision data, AuthAdvisor automates approval for payers, medical benefits managers, and other vendors to deliver decisions within seconds rather than days, and reduces manual reviews by over 50%.

According to the Council for Affordable Quality Healthcare (CAQH), "the cost to complete a prior authorization remains the single highest cost for the healthcare industry at $13.40 per manual transaction and $7.19 per partially electronic web portal transaction." Not only is it costly, but it is also an administrative burden with manual reviews sometimes taking days or weeks, which delays patient treatments, creates obstacles to care, and potentially negatively impacts clinical outcomes.

Apixio's AuthAdvisor solves these problems by automatically approving diagnostics and procedures based on historical data and decisions made by the provider and payer.

"This is a new way to use data science to accelerate one of the most burdensome aspects of healthcare delivery," said Apixio CEO Sachin Patel. "AuthAdvisor relies on the accuracy of an organization's past decisions to process approvals, rather than relying on rules-based approaches that are tedious to maintain and often result in a high number of manual reviews. With AuthAdvisor, approvals are delivered at the speed and scale that today's high-performance healthcare environments demand."

With Apixio's Apicare AuthAdvisor solution, organizations can:

"The AuthAdvisor system is transparent and customizable, giving payers and benefits managers the visibility and flexibility they need to feel confident in the decisions being made," Patel said. "The latest addition to our AI platform, this technology has the potential to not only save tremendous time and money, but also greatly improve care delivery and member satisfaction for millions of Americans."

AuthAdvisor is already active in 16 states, automating authorization requests for over 4,000 different procedures. Apixio will be showcasing its value-based care platform, including Apicare AuthAdvisor, at both RISE National 2022 on March 7-9 in Nashville and HIMSS 2022 at booth #1579 on March 14-18 in Orlando.

To learn more about the Apicare AuthAdivsor solution, visit http://www.apixio.com/apicare-authadvisor/.

About ApixioApixio is advancing healthcare with data-driven intelligence and analytics. Our Artificial Intelligence platform gives organizations across the healthcare spectrum the power to mine clinical information at scale, creating novel insights that will change the way healthcare is measured, care is delivered, and discoveries are made. Learn more atwww.apixio.com.

MEDIA CONTACT:Kerri TarantoNext PR[emailprotected]

SOURCE Apixio Inc.

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Apixio's New Apicare AuthAdvisor Leverages Machine Learning, Predictive Decision-Making to Automate Approvals & Reduce Manual Workload by more...