Mediaboss Marketing

For Immediate Release: March 19, 2021

Today, the U.S. Food and Drug Administration issued an emergency use authorization (EUA) for the first machine learning-based Coronavirus Disease 2019 (COVID-19) non-diagnostic screening device that identifies certain biomarkers that are indicative of some types of conditions, such as hypercoagulation (a condition causing blood to clot more easily than normal).

The Tiger Tech COVID Plus Monitor is intended for use by trained personnel to help prevent exposure to and spread of SARS-CoV-2, the virus that causes COVID-19. The device identifies certain biomarkers that may be indicative of SARS-CoV-2 infection as well as other hypercoagulable conditions (such as sepsis or cancer) or hyper-inflammatory states (such as severe allergic reactions), in asymptomatic individuals over the age of 5. The Tiger Tech COVID Plus Monitor is designed for use following a temperature reading that does not meet criteria for fever in settings where temperature check is being conducted in accordance with Centers for Disease Control and Prevention (CDC) and local institutional infection prevention and control guidelines. This device is not a substitute for a COVID-19 diagnostic test and is not intended for use in individuals with symptoms of COVID-19.

The FDA is committed to continuing to support innovative methods to fight the COVID-19 pandemic through new screening tools, said Jeff Shuren, M.D., J.D., director of FDAs Center for Devices and Radiological Health. Combining use of this new screening device, that can indicate the presence of certain biomarkers, with temperature checks could help identify individuals who may be infected with the virus, thus helping to reduce the spread of COVID-19 in a wide variety of public settings, including healthcare facilities, schools, workplaces, theme parks, stadiums and airports. The device is an armband with embedded light sensors and a small computer processor. The armband is wrapped around a persons bare left arm above the elbow during use. The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes. Once the measurement is completed, the processor extracts some key features of the pulsatile signals, such as pulse rate, and feeds them into a probabilistic machine learning model that has been trained to make predictions on whether the individual is showing certain signals, such as hypercoagulation in blood. Hypercoagulation is known to be a common abnormality in COVID-19 patients. The result is provided in the form of different colored lights used to indicate if an individual is demonstrating certain biomarkers, or if the result is inconclusive.

The clinical performance of the Tiger Tech COVID Plus Monitor was studied in hospital and school settings. The hospital study, which was considered a validation study, enrolled 467 asymptomatic individuals, including 69 confirmed positive cases, and demonstrated that the Tiger Tech COVID Plus Monitor had a positive percent agreement (proportion of the COVID-19 positive individuals identified correctly by the device to possess certain biomarkers) of 98.6% and a negative percent agreement (proportion of the COVID-19 negative individuals identified correctly by the device to not possess certain biomarkers) of 94.5%. The school study, which was considered a confirmatory study, showed similar performance.

The Tiger Tech COVID Plus Monitor is not a diagnostic device and must not be used to diagnose or exclude SARS-CoV-2 infection. The device is intended for use on individuals without a fever. An individuals underlying condition may interfere with the COVID-19 related performance of the device and could lead to an incorrect screening result.

The FDA issued the EUA to Tiger Tech Solutions, Inc.

The FDA, an agency within the U.S. Department of Health and Human Services, protects the public health by assuring the safety, effectiveness, and security of human and veterinary drugs, vaccines, and other biological products for human use, and medical devices. The agency also is responsible for the safety and security of our nations food supply, cosmetics, dietary supplements, products that give off electronic radiation, and for regulating tobacco products.

###

03/19/2021

Read the original here:
Coronavirus (COVID-19) Update: FDA Authorizes First Machine Learning-Based Screening Device to Identify Certain Biomarkers That May Indicate COVID-19...

OAKLAND, Calif.--(BUSINESS WIRE)-- Dascena, Inc., a machine learning diagnostic algorithm company that is targeting early disease intervention to improve patient care outcomes, today announced the publication in Kidney International Reports of results from a study evaluating the companys machine learning algorithm, PreviseTM, for the earlier prediction of acute kidney injury (AKI). Findings showed that Previse was able to predict the onset of AKI sooner than the standard hospital systems, XGBoost AKI prediction model and the Sequential Organ Failure Assessment (SOFA), up to 48 hours in advance of onset. Previse has previously received Breakthrough Device designation from the U.S. Food and Drug Administration (FDA).

AKI is a severe and complex condition that presents in many hospitalized patients, yet it is often diagnosed too late, resulting in significant kidney injury with no effective treatments to reverse damage and restore kidney function, said David Ledbetter, chief clinical officer of Dascena. If we are able to predict AKI onset earlier, physicians may be able to intervene sooner, reducing the damaging effects. These findings with Previse are exciting and further demonstrate the role we believe machine learning algorithms can play in disease prediction. Further, with Breakthrough Device designation from the FDA, we hope to continue to efficiently advance Previse through clinical studies so that we may be able to positively impact as many patients as possible through earlier detection.

The study was conducted to evaluate the ability of Previse to predict for Stage 2 or 3 AKI, as defined by KDIGO guidelines, compared to XGBoost and SOFA. Using convolutional neural networks (CNN) and patient Electronic Health Record (EHR) data, 12,347 patient encounters were analyzed, and measurements included Area Under the Receiver Operating Characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advanced prediction of AKI onset. Findings from the study demonstrated that on a hold-out test set, the algorithm attained an AUROC of 0.86, compared to 0.65 and 0.70 for XGBoost and SOFA, respectively, and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window prior to onset.

About Previse

Previse is an algorithm that continuously monitors hospitalized patients and can predict acute kidney injury more than a full day before patients meet the clinical criteria for diagnosis, providing clinicians with ample time to intervene and prevent long-term injury.

About Dascena

Dascena is developing machine learning diagnostic algorithms to enable early disease intervention and improve care outcomes for patients. For more information, visit dascena.com

View source version on businesswire.com: https://www.businesswire.com/news/home/20210316005151/en/

See the rest here:
Dascena Announces Publication of Results From Its Machine Learning Algorithm for Prediction of Acute Kidney Injury in Kidney International Reports -...

The GlobalMachine Learning as a Service (MLaaS)Market 2021 by Company, Regions, Type and Application, Forecast to 2025 introduced byIndustryandresearch.compresents an excellent vision on the global market, delivering a information analysis of distinct factors associated with the report. Likewise, this analysis offers broad insights into technological spending across the forecast period, providing a unique view point on the global Machine Learning as a Service (MLaaS) market across each of the categories including in the survey. The global review of the keyword industry assists clients in assessing business challenges and prospects. The research includes the most recent keyword business forecast analysis for the time period in question.Furthermore, The Machine Learning as a Service (MLaaS) market report delivers a wide range of details of various aspects of the Machine Learning as a Service (MLaaS) industry such as the growth strategies, segmentation by product type, applications, regions, and key players. Along with key players, the Machine Learning as a Service (MLaaS) market report includes company perspectives and marketing strategies of the leading companies.

The report helps market players to Machine Learning as a Service (MLaaS) market events, figures, and analysis at a minute level and drive their businesses accordingly. The report also incorporates a significant evaluation of market share, size, demand, production volume, sales revenue, and annual growth rates. It features a dashboard overview of leading companies encompassing their successful marketing strategies, market contribution, recent developments in both historic and present situation. The report offers a detailed overview of this global Machine Learning as a Service (MLaaS) industry landscape including insights pertaining to growth factors, limitations, opportunities, and other prospects influencing the business scenario.

(Exclusive Offer: 20% discount on this report) Get | Download Sample Copy @https://www.industryandresearch.com/report/Global-Machine-Learning-as-a-ServiceMLaaSMarket-Growth-In-depth-InsightKey-Research-Finding-to-20202026/233439#samplereport

NOTE:Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.

The Machine Learning as a Service (MLaaS) Market research report offers an exhaustive analysis of this business space. The key trends that define the Machine Learning as a Service (MLaaS) Industry market during the analysis timeframe are mentioned in the report, alongside other factors such as regional scope and regulatory outlook. Also, the document elaborates on the impact of current industry trends on key market driving factors as well as top challenges. The Machine Learning as a Service (MLaaS) market report provides a granular assessment of the business space, while elaborating on all the segments of this business space. The document offers key insights pertaining to the market players as well as their gross earnings. Moreover, details regarding the regional scope and the competitive scenario are entailed in the study.

Other Features of The Report:

The prominent players of the market are:Microsoft, International Business Machine Corporation, Amazon Web Services, Google, Inc., Bigml, Inc., Fico, Hewlett-Packard Enterprise Development Lp..

The study is segmented by the following product type: ,Small And Medium Enterprises, Large Enterprises

Major applications/end-users industry are as follows: ,Education, Banking And Financial Services, Insurance, Automotive And Transportation, Healthcare

Regions covered in the report:Americas(United States, Canada, Mexico, and Brazil),APAC(China, Japan, Korea, Southeast Asia,India, and Australia),Europe(Germany, France, UK, Italy, Russia, and Spain),and Middle East & Africa(Egypt, South Africa, Israel, Turkey, and GCC Countries).

Impact of COVID-19 on Machine Learning as a Service (MLaaS) Market Industry:The coronavirus downturn is a financial downturn occurring across the world economy in 2021 because of the COVID-19 pandemic. The pandemic could influence three primary parts of the worldwide economy: creation, inventory network, and firms and monetary business sectors. The report offers total form of the Machine Learning as a Service (MLaaS) Market will incorporate the effect of the COVID-19 and foreseen change on the future standpoint of the business, by considering the political, monetary, social and innovative boundaries.

Furthermore, the report has included the new project, key development areas, product specification, SWOT analysis, investment feasibility analysis, return analysis, and development trends. It gives a detailed global Machine Learning as a Service (MLaaS) market share perspective combined with strategic recommendations, based on the emerging segments. For a better understanding and comprehensive analysis of the market, the key segments have also been divided into sub-segments.

Top-Rated Pointers from the Industry Market Report:

Machine Learning as a Service (MLaaS) market is a comprehensive collection of details and figures in the form of graphs, pie charts, and tables. Data is specifically acquired from secondary sources including the internet, journals, magazines, and press releases. All the retrieved data is validated using primary interviews and questionnaires. This report offers a detailed view of market opportunity by end user segments, product segments, sales channels, key countries, and import / export dynamics. It details market size & forecast, growth drivers, emerging trends, market opportunities, and investment risks in over various segments in Machine Learning as a Service (MLaaS) industry. It provides a comprehensive understanding of Machine Learning as a Service (MLaaS) market dynamics in both value and volume terms.

ACCESS FULL REPORT: @https://www.industryandresearch.com/report/Global-Machine-Learning-as-a-ServiceMLaaSMarket-Growth-In-depth-InsightKey-Research-Finding-to-20202026/233439

The readers of the report can also extract several key insights such as market size of varies products and application along with their market share and growth rate. The report also includes information for next five years as forested data and past five years as historical data and the market share of the several key information. The Machine Learning as a Service (MLaaS) market report outlines information on the key geographies, market landscapes alongside the production and consumption analysis, supply and demand analysis, market growth rate, besides the future forecast, etc. This report also provides SWOT and Porters Five Forces Analysis, investment feasibility and return analysis.

Key Benefits of Buying a Report The report

assists market leaders / new entrants with information on the closest estimate of revenue for the artificial intelligence market and its subsegments in overall Machine Learning as a Service (MLaaS). This report helps stakeholders understand the competitive environment, better position their businesses, and gain more insight into planning the right market development strategies. The report also helps stakeholders understand market trends and provide information on key market momentum, constraints, challenges, and opportunities.

(If you have special needs, please let us know ([emailprotected]). We will provide you with reports as needed.)

See the original post:
Global Machine Learning as a Service (MLaaS) Market 2021 Future Growth Prospect, Industry Report And Growing Demand Analysis Till 2025 | Microsoft,...

Thelatest survey on Machine Learning Software MarketIndustry managedvarious organizations of the industry from different geographies or regions.The Report study consists of qualitative and quantitative information highlighting key market developments challenges that industry and competition are facing along with gap analysis, new opportunities available and trend also include COVID-19 impact Analysis in Machine Learning Software Market andimpact various factors resulting in boosting Machine Learning Software Market at global as well as regional level. There are huge competitions that take place worldwide and must require the study ofMARKET ANALYSISsuch as Top Competitors /Top Players are:Microsoft, Google, TensorFlow, Kount, Warwick Analytics, Valohai, Torch, Apache SINGA, AWS, BigML, Figure Eight, Floyd Labs.Porters Five Forces Analysis, impact analysis of covid-19, and SWOT Analysisare also mentioned tounderstand the factors impacting consumer and supplier behavior.

Download FREE PDF Sample Copy of Machine Learning Software Market @https://www.syndicatemarketresearch.com/sample/machine-learning-software-market

Dont miss out on business opportunities in Machine Learning Software Market. Speak to our analyst and gain crucial industry insights that will help your business growth while filling Free PDF Sample Reports

We are here to implement a Free PDF Sample Report copy as per your Research Requirement, also including impact analysisofCOVID-19 on Machine Learning Software Market Size

Key Highlights of the TOC provided by Syndicate Market Research:

Major Product Type of Machine Learning Software Covered in Market Research report:On-Premises, Cloud Based

Application Segments Covered in Market Research Report:Large Enterprises, SMEs

Global Machine Learning Software Industry Market: By Region

North America

Europe

Asia Pacific

Latin America

The Middle East and Africa

Competitive Market Share

In terms of Machine Learning Software market, Microsoft, Google, TensorFlow, Kount, Warwick Analytics, Valohai, Torch, Apache SINGA, AWS, BigML, Figure Eight, Floyd Labs are the top players operating in the global market. These behemoths have implemented key business strategies such as product innovation, strategic partnerships & collaborations, new product launches, new service launches, joint ventures, and contracts to reinforce their market position along with gaining a huge chunk of the market share.

In addition, the report also covers key strategic developments of the market including acquisitions & mergers, new type launch, agreements, partnerships, collaborations & joint ventures, research & development, regional expansion of major participants involved in the Machine Learning Software market on a global and regional basis.

TOC include below Mentioned Featured Points:

Chapter 1:: Report Overview

Chapter 2:: Market Snapshot

2.1 Major Companies Overview

2.2 Machine Learning Software Market Concentration

2.3 Six-Year Compound Annual Growth Rate (CAGR)

Chapter 3:: Value Chain of Machine Learning Software Market

3.1 Upstream

3.2 Downstream

3.3 Porters & Five Forces Analysis and SWOT Analysis

Chapter 4:: Players Profiles

4.1 Company Profiles

4.2 Product Introduction

4.3 Production, Revenue

4.4 SWOT Analysis

Chapter 5:: Global Machine Learning Software Market Analysis by Regions

5.1 Machine Learning Software Market Status and Prospect

5.2 Machine Learning Software Market Size and Growth Rate

5.3 Machine Learning Software Market Local Capacity, Import, Export, Local Consumption Analysis

Chapter 6:: North America Machine Learning Software Market Analysis by Countries

Chapter 7:: China Machine Learning Software Market Analysis by Countries

Chapter 8:: Europe Machine Learning Software Market Analysis by Countries

Chapter 9:: Asia-Pacific Machine Learning Software Market Analysis by Countries

Chapter 10:: India Machine Learning Software Market Analysis by Countries

Chapter 11:: the Middle East and Africa Machine Learning Software Market Analysis by Countries

Chapter 12:: South America Machine Learning Software Market Analysis by Countries

Chapter 13:: Global Machine Learning Software Market Segment by Types

Chapter 14:: Global Machine Learning Software Market Segment by Applications

Chapter 15:: Machine Learning Software Market Forecast by Regions

Chapter 16:: Appendix

https://melvinasmarketblogs.blogspot.com/2021/01/leasing-automation-software-market-2020.html

About Syndicate Market Research:

At Syndicate Market Research, we provide reports about a range of industries such as healthcare & pharma, automotive, IT, insurance, security, packaging, electronics & semiconductors, medical devices, food & beverage, software & services, manufacturing & construction, defense aerospace, agriculture, consumer goods & retailing, and so on. Every aspect of the market is covered in the report along with its regional data. Syndicate Market Research committed to the requirements of our clients, offering tailored solutions best suitable for strategy development and execution to get substantial results. Above this, we will be available for our clients 247.

Syndicate Market Research244 Fifth Avenue, Suite N202New York, 10001, United StatesWebsite:https://www.syndicatemarketresearch.com/Blog:Syndicate Market Research Blog

View original post here:
Global Machine Learning Software Market Detailed Survey and Outlook Report Shows How Top Companies Is Able to Survive in Future FLA News - FLA News

With the fundamental technologies of theMachine Economy IoT, blockchain, and machine learning gathering, analyzing, and storing big data, businesses are turning critical information into actionable insights.

As these complex technologies continue to evolve and fields of innovation unfold, we are beginning to see the convergence of three major trends in the Machine Economy:

Machine-to-Machine (M2M) enables and supports communication between machines and devices through both wireless and wired systems. M2M relates to connecting, remote monitoring, sensing, and actuating devices at its most basic level. Overall, M2M communication, along with Machine Economys underpinning technologies, is projected to rise over thenext five to ten yearsby compound annual growth rates of 10% to 50%.

M2M connectivity will dramatically change the way we think about business assets and the future workforce, along with the skill sets required to succeed in a future automated economy. When machines serve and transact with other machines, the opportunity to create new value arises far beyond optimizing transactions for a single system user but rather for the aggregation of machines across a network.

The Machine Economy majorly impacts the way we design technological solutions. Still, to unleash its full potential, we need to understand where we are in terms of development with realistic expectations of when businesses can program machines to transact and participate autonomously, securely independently, and more reliably than today.

Smart machines are embedded with cognitive computing systems that use articial intelligence and machine learning algorithms to sense, learn, solve, and interact differently without the need for human intervention. Sensors, self-monitoring tools, and communication capabilities enable machines to produce unanticipated results by gathering and analyzing large data sets.

The machines that weve built to support us are now able to do more; improving operational efficiencies, decreasing costs, and mitigating business risks.

IoT, blockchain, and machine learning form the technology stack of the Machine Economy. With the use of sensors and intrinsic knowledge regarding its capabilities and features, self-monitoring machines can record and report on the status of its key components and environmental conditions. Embedded intelligence systems allow machines to automate decision-making and adapt parameters within defined business rules, ordering services like maintenance and repair.

Businesses are moving away from buying machines outright (CapEx to OpEx). Instead, they will see new business models emerge with self-managed assets (servitization) sharing their services in a distributed ecosystem. Business owners will no longer define value through ownership and machine subscription models; pay-per-outcome and real-time leasing will be prevalent. New marketplaces will be established based on the activity of acquiring, providing, or sharing access to goods and services, often facilitated by a community-based online platform to facilitate collaboration.

Spurred primarily by the growth in M2M connections, we are beginning to see a change in economic systems where machines communicate with each other independently, coordinate orders, execute transactions, and conclude contracts. Machines will become self-sovereign agents with their own identity and history. Machine needs and opportunities will primarily be identified by mining and analyzing data from M2M transactions and environmental information such as their condition, location, and performance level. As autonomous market participants, machines will become financial actors in their own right, with bank accounts and payment systems.

Technology is laying the foundation for a future of automated machines, trustless smart contracts, and interconnected sensors all with the end goal of improving human lives.

With the natural progression of technology, we are already well on our way toward the Machine Economy. The majority of businesses are firmly situated between the second and third steps, and we can expect the adoption and impact path to gain momentum steadily. Machines eventually become self-sufficient entities and autonomous market participants, executing end-to-end transactions safely, securely, and more efficiently than in historical systems affected by human error.

The Machine Economy promises to multiply our capacity to work smarter, more efficiently, and seamlessly through technology. The transition from companies selling products and services to selling measurable outcomes will redefine business strategies and the base of competitive advantage. We can simplify and execute precision operations through automation whilst laying the groundwork for more complex products to emerge.

As the Machine Economy becomes more ingrained in every industry, it will be defined by real-time demand sensing, high-levels of automation, and flexible production systems through the pervasive use of intelligent machines to complement human labor (machine augmentation).

Delivering outcomes will require companies to forge new ecosystem partnerships centered on customer needs rather than individual products or services. Although we still have a long way to go, we are already on a path where autonomous machines will have the power to make their own decisions, buy and sell services, and participate in the future economy as a new asset class of market participants.

With the rising importance of data collection, data analysis, and data security, businesses will need to innovate new offerings and expand their capabilities and ecosystems to compete in this emerging marketplace.

In the future, we willown much less(assets and machines) andshare much more(services and information) to create a new order of value where efficiency, productivity, and return on investment (ROI) reign supreme this is the Machine Economy.

Read more:
Four Steps in the Evolution of the Machine Economy - IoT For All