The recent report on GlobalMindfulness Meditation Apps Industry Market Report 2021 by Key Players, Types, Applications, Countries, Market Size, Forecast to 2027 offered by Credible Markets, comprises of a comprehensive investigation into the geographical landscape, industry size along with the revenue estimation of the business. Additionally, the report also highlights the challenges impeding market growth and expansion strategies employed by leading companies in the Mindfulness Meditation Apps Industry Market.

An exhaustive competition analysis that covers insightful data on industry leaders is intended to help potential market entrants and existing players in competition with the right direction to arrive at their decisions. Market structure analysis discusses in detail Mindfulness Meditation Apps Industry companies with their profiles, revenue shares in market, comprehensive portfolio of their offerings, networking and distribution strategies, regional market footprints, and much more.

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The report primarily attempts to track the evolution of growth path of market from 2019, through 2021, and post the crisis. It also provides long-term market growth projections for a predefined period of assessment, 2015 2027. Based on detailed analysis of industrys key dynamics and segmental performance, the report offers an extensive assessment of demand, supply, and manufacturing scenario.

Key players in the global Mindfulness Meditation Apps market covered in Chapter 12:

The Mindfulness App Smiling Mind Ten Percent Happier Insights Network, Inc. Inner Explorer, Inc. Committee for Children Deep Relax Breethe Stop, Breathe & Think Mindfulness Everywhere Ltd.

In Chapter 4 and 14.1, on the basis of types, the Mindfulness Meditation Apps market from 2015 to 2025 is primarily split into:

IOS Android Others

In Chapter 5 and 14.2, on the basis of applications, the Mindfulness Meditation Apps market from 2015 to 2025 covers:

0 5 Years 6 12 Years 13 18 Years 19 Years and Above

Geographically, the detailed analysis of consumption, revenue, market share and growth rate, historic and forecast (2015-2027) of the following regions:United States, Canada, Germany, UK, France, Italy, Spain, Russia, Netherlands, Turkey, Switzerland, Sweden, Poland, Belgium, China, Japan, South Korea, Australia, India, Taiwan, Indonesia, Thailand, Philippines, Malaysia, Brazil, Mexico, Argentina, Columbia, Chile, Saudi Arabia, UAE, Egypt, Nigeria, South Africa and Rest of the World

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Global Mindfulness Meditation Apps Industry Market Report 2021 by Key Players, Types, Applications, Countries, Market Size, Forecast to 2027

Chapter 1 Mindfulness Meditation Apps Industry Introduction and Market Overview

Chapter 2 Executive Summary

Chapter 3 Industry Chain Analysis

Chapter 4 Global Mindfulness Meditation Apps Industry Market, by Type

Chapter 5 Mindfulness Meditation Apps Industry Market, by Application

Chapter 6 Global Mindfulness Meditation Apps Industry Market Analysis by Regions

Chapter 7 North America Mindfulness Meditation Apps Industry Market Analysis by Countries

Chapter 8 Europe Mindfulness Meditation Apps Industry Market Analysis by Countries

Chapter 9 Asia Pacific Mindfulness Meditation Apps Industry Market Analysis by Countries

Chapter 10 Middle East and Africa Mindfulness Meditation Apps Industry Market Analysis by Countries

Chapter 11 South America Mindfulness Meditation Apps Industry Market Analysis by Countries

Chapter 12 Competitive Landscape

Chapter 13 Industry Outlook

Chapter 14 Global Mindfulness Meditation Apps Industry Market Forecast

Chapter 15 New Project Feasibility Analysis

Points Covered in the Report

The points that are discussed within the report are the major market players that are involved in the market such as market players, raw material suppliers, equipment suppliers, end users, traders, distributors and etc.

The complete profile of the companies is mentioned. And the capacity, production, price, revenue, cost, gross, gross margin, sales volume, sales revenue, consumption, growth rate, import, export, supply, future strategies, and the technological developments that they are making are also included within the report. This report analysed 12 years data history and forecast.

The growth factors of the market are discussed in detail wherein the different end users of the market are explained in detail.

Data and information by market player, by region, by type, by application and etc., and custom research can be added according to specific requirements.

The report contains the SWOT analysis of the market. Finally, the report contains the conclusion part where the opinions of the industrial experts are included.

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Impact of Covid-19 in Mindfulness Meditation Apps Industry Market:Since the COVID-19 virus outbreak in December 2019, the disease has spread to almost every country around the globe with the World Health Organization declaring it a public health emergency. The global impacts of the coronavirus disease 2019 (COVID-19) are already starting to be felt, and will significantly affect the Mindfulness Meditation Apps Industry market in 2021. The outbreak of COVID-19 has brought effects on many aspects, like flight cancellations; travel bans and quarantines; restaurants closed; all indoor/outdoor events restricted; over forty countries state of emergency declared; massive slowing of the supply chain; stock market volatility; falling business confidence, growing panic among the population, and uncertainty about future.

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Global Mindfulness Meditation Apps Market Report: Industry Trends, Share, Size, Growth, Opportunity and Forecast 2021-2027 - NeighborWebSJ

Meditation marketis expected to gain market growth in the forecast period of 2020 to 2027. Data Bridge Market Research analyses the market to account to USD 9.0 billion by 2027 growing at a CAGR of 10.40% in the above-mentioned forecast period. The growing awareness amongst the individual and world population about the hidden power of meditation and its unconventional outcomes for the health benefits to lead a healthy life is driving the market growth exponentially in the forecast period of 2020 to 2027.

Meditation is a professional and in depth market report that focuses on primary and secondary drivers, market share, possible sales volume, leading segments and geographical analysis. Businesses can achieve unrivalled insights and acquaintance of the best market opportunities into their respective markets with the help of this Meditation market report. All the major topics of the market research analysis are covered here that includes market definition, market segmentation, competitive analysis, major developments in the market, and top-notch research methodology. By leveraging the global experience of industry analysts, consultants and domain experts, this Meditation report has been prepared and delivered with excellence.

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The major players covered in the meditation market report areSimply Better Apps, Smiling Mind, Inner Explorer, Inc., Committee for Children., Stop, Breathe & Think PBC, Breethe. Life , Simple Habit, Inc., Calm. HEADSPACE INC, MINDSET Brain Gym Toronto Inc, Inscape among other domestic and global players. Market share data is available for Global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America separately. DBMR analyst understands competitive strengths and provides competitive analysis for each competitor separately.

Market Drivers:

The up-and-coming tradition of thoughtful awareness is serving in inclination of organizations endeavoring mindfulness meditation. The swelling predominance of subconscious health dysfunctions, such as mood complications and anxiety troubles, over multiple age assemblies are rising in an extensive shift to the meditation market

This meditation market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research meditation market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Table of Contents:

1. Introduction 2. Market Segmentation 3. Market Overview 4. Executive Summary 5. Premium Insights 6. By Component 7. Product Type 8. Delivery 9. Industry Type 10. Geography

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Global Meditation Market:Segmentation

By Product (Meditation Programs, Yoga Centers, Apps, Websites, Books, Online Courses, Workshops),

Mental Disorder (Mood Disorders, Anxiety Disorders),

Type (Focused Attention, Open Monitoring, Self-Transcending Meditation),

Meditation Type (Sophrology, Kundalini Yoga, Mindful Fitness Surges),

Information Source (Books, Newspapers, Internet, DVDs, Articles)

Country (U.S., Canada, Mexico, Germany, Italy, U.K., France, Spain, Netherland, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia- Pacific, Brazil, Argentina, Rest of South America, South Africa, Saudi Arabia, UAE, Egypt, Israel, Rest of Middle East & Africa)

Opportunities in the market

To describe and forecast the market, in terms of value, for various segments, by region North America, Europe, Asia Pacific (APAC), and Rest of the World (RoW) The key findings and recommendations highlight crucial progressive industry trends in the Meditation Market, thereby allowing players to develop effective long term strategies

To strategically profile key players and comprehensively analyze their market position in terms of ranking and core competencies, and detail the competitive landscape for market leaders Extensive analysis of the key segments of the industry helps in understanding the trends in types of Meditation across Glob. To get a comprehensive overview of the Meditation market.

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Global Meditation Market Insights and Forecast Up To 2027 ||Committee for Children., Stop, Breathe & Think PBC, Breethe. Life , Simple Habit -...

You have heard that it was said, You shall love your neighbor and hate your enemy. But I say to you, love your enemies, bless those who curse you, do good to those who hate you, and pray for those who spitefully use you and persecute you, that you may be sons of your Father in heaven; for He makes His sun rise on the evil and on the good, and sends rain on the just and on the unjust. For if you love those who love you, what reward have you? Do not even the tax collectors do the same? And if you greet your brethren only, what do you do more than others? Do not even the tax collectors do so? Therefore you shall be perfect, just as your Father in heaven is perfect. Matthew 5:43-48

The Bible says we must forgive those who have done us harm. How do we do that? We say, Ill forgive them but I will never forget.

If you dont forget, have you truly let it go? Is that the way you want God to forgive your sins?

Oh, what joy for those whose disobedience is forgiven, whose sin is put out of sight!

Yes, what joy for those whose record the Lord has cleared of guilt, whose lives are lived in complete honesty!

If there was ever anyone who had a right to claim mistreatment, it was Jesus. What cruel physical torture, indignities and mockery He suffered before His death and what was His response? Father forgive them.

I have never, and neither have you, had to go through what Jesus did. Remember what God forgave you and what it cost Him before you decide you cant forgive someone for what they have done to you.

In the prayer the Lord taught His disciples was, forgive us our sins, as we have forgiven those who sin against us (NLT). Isnt that saying, God forgive us in the same way we forgive those who have harmed us? That alone is enough reason to put great effort into forgiveness.

I have found that when forgiving is difficult, it is praying for that person that will finally clear my heart. When we first begin to pray for them, it may be very difficult but if we keep returning to the prayer, God will honor it and one day we will find that our heart is clear of any hard feelings and we sincerely desire good for them.

The act of forgiveness benefits us greatly.

There is an enormous physical burden to being hurt and disappointed, says Karen Swartz, M.D., director of the Mood Disorders Adult Consultation Clinic at The Johns Hopkins Hospital. Chronic anger puts you into a fight-or-flight mode, which results in numerous changes in heart rate, blood pressure and immune response. Those changes, then, increase the risk of depression, heart disease and diabetes, among other conditions. :

She continued, Forgiveness, however, calms stress levels, leading to improved health. Studies have found that the act of forgiveness can reap huge rewards for your health, lowering the risk of heart attack; improving cholesterol levels and sleep; and reducing pain, blood pressure, and levels of anxiety, depression and stress.

Did you know there were such physical and mental rewards when we obey God?

Sylvia Hughes is a longtime Sunday School and womens Bible study teacher, and a retired newspaper editor. She can be reached via email at blameditations@gmail.com.

Thadd White is Editor of the Bertie Ledger-Advance and can be reached via email at twhite@ncweeklies.com.

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Meditating on His Word: Forgiveness is good for body and soul - The Daily Advance

ST. PAUL, Minn., Jan. 19, 2021 /PRNewswire/ -- San Diego-based Mission Healthcare, one of the largest home health, hospice, and palliative care providers in California, will adopt Muse Healthcare's machine learning and predictive modeling tool to help deliver a more personalized level of care to their patients.

The Muse technology evaluates and models every clinical assessment, medication, vital sign, and other relevant data to perform a risk stratification of these patients. The tool then highlights the patients with the most critical needs and visually alerts the agency to perform additional care. Muse Healthcare identifies patients as "Critical," which means they have a greater than 90% likelihood of passing in the next 7-10 days. Users are also able to make accurate changes to care plans based on the condition and location of the patient. When agencies use Muse's powerful machine learning tool, they have an advantage and data proven outcomes to demonstrate they are providing more care and better care to patients in transition.

According to Mission Healthcare's Vice President of Clinical and Quality, Gerry Smith, RN, MSN, Muse will serve as an invaluable tool that will assist their clinicians to enhance care for their patients. "Mission Hospice strives to ensure every patient receives the care and comfort they need while on service, and especially in their final days. We are so excited that the Muse technology will provide our clinical team with additional insights to positively optimize care for patients at the end of life. This predictive modeling technology will enable us to intervene earlier; make better decisions for more personalized care; empower staff; and ultimately improve patient outcomes."

Mission Healthcare's CEO, Paul VerHoeve, also believes that the Muse technology will empower their staff to provide better care for patients. "Predictive analytics are a new wave in hospice innovation and Muse's technology will be a valuable asset to augment our clinical efforts at Mission Healthcare. By implementing a revolutionary machine learning tool like Muse, we can ensure our patients are receiving enhanced hands-on care in those critical last 7 10 days of life. Our mission is to take care of people, with Muse we will continue to improve the patient experience and provide better care in the final days and hours of a patient's life."

As the only machine learning tool in the hospice industry, the Muse transitions tool takes advantage of the implemented documentation within the EMR. This allows the agency to quickly implement the tool without disruption. "With guidance from our customers in the hundreds of locations that are now using the tool, we have focused on deploying time saving enhancements to simplify a clinician's role within hospice agencies. These tools allow the user to view a clinical snapshot, complete review of the scheduled frequency, and quickly identify the patients that need immediate attention. Without Muse HC, a full medical review must be conducted to identify these patients," said Tom Maxwell, co-Founder of Muse Healthcare. "We are saving clinicians time in their day, simplifying the identification challenges of hospice, and making it easier to provide better care to our patients. Hospice agencies only get one chance to get this right," said Maxwell.

CEO of Muse Healthcare, Bryan Mosher, is also excited about Mission's adoption of the Muse tool. "We welcome the Mission Healthcare team to the Muse Healthcare family of customers, and are happy to have them adopt our product so quickly. We are sure with the use of our tools,clinicians at Mission Healthcare will provide better care for their hospice patients," said Mosher.

About Mission Healthcare

As one of the largest regional home health, hospice, and palliative care providers in California, San Diego-based Mission Healthcare was founded in 2009 with the creation of its first service line, Mission Home Health. In 2011, Mission added its hospice service line. Today, Mission employs over 600 people and serves both home health and hospice patients through Southern California. In 2018, Mission was selected as a Top Workplace by the San Diego Union-Tribune. For more information visit https://homewithmission.com/.

About Muse Healthcare

Muse Healthcare was founded in 2019 by three leading hospice industry professionals -- Jennifer Maxwell, Tom Maxwell, and Bryan Mosher. Their mission is to equip clinicians with world-class analytics to ensure every hospice patient transitions with unparalleled quality and dignity. Muse's predictive model considers hundreds of thousands of data points from numerous visits to identify which hospice patients are most likely to transition within 7-12 days. The science that powers Muse is considered a true deep learning neural network the only one of its kind in the hospice space. When hospice care providers can more accurately predict when their patients will transition, they can ensure their patients and the patients' families receive the care that matters most in the final days and hours of a patient's life. For more information visit http://www.musehc.com.

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Mission Healthcare of San Diego Adopts Muse Healthcare's Machine Learning Tool - Southernminn.com

ATLANTACompared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.

Deep learning, built on advanced neural networks, can characterize these relationships by combining and analyzing data from many sources. At the Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State researchers are using deep learning to learn more about how mental illness and other disorders affect the brain.

Although deep learning models have been used to solve problems and answer questions in a number of different fields, some experts remain skeptical. Recent critical commentaries have unfavorably compared deep learning with standard machine learning approaches for analyzing brain imaging data.

However, as demonstrated in the study, these conclusions are often based on pre-processed input that deprive deep learning of its main advantagethe ability to learn from the data with little to no preprocessing. Anees Abrol, research scientist at TReNDS and the lead author on the paper, compared representative models from classical machine learning and deep learning, and found that if trained properly, the deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

We compared these models side-by-side, observing statistical protocols so everything is apples to apples. And we show that deep learning models perform better, as expected, said co-author Sergey Plis, director of machine learning at TReNDS and associate professor of computer science.

Plis said there are some cases where standard machine learning can outperform deep learning. For example, diagnostic algorithms that plug in single-number measurements such as a patients body temperature or whether the patient smokes cigarettes would work better using classical machine learning approaches.

If your application involves analyzing images or if it involves a large array of data that cant really be distilled into a simple measurement without losing information, deep learning can help, Plis said.. These models are made for really complex problems that require bringing in a lot of experience and intuition.

The downside of deep learning models is they are data hungry at the outset and must be trained on lots of information. But once these models are trained, said co-author Vince Calhoun, director of TReNDS and Distinguished University Professor of Psychology, they are just as effective at analyzing reams of complex data as they are at answering simple questions.

Interestingly, in our study we looked at sample sizes from 100 to 10,000 and in all cases the deep learning approaches were doing better, he said.

Another advantage is that scientists can reverse analyze deep-learning models to understand how they are reaching conclusions about the data. As the published study shows, the trained deep learning models learn to identify meaningful brain biomarkers.

These models are learning on their own, so we can uncover the defining characteristics that theyre looking into that allows them to be accurate, Abrol said. We can check the data points a model is analyzing and then compare it to the literature to see what the model has found outside of where we told it to look.

The researchers envision that deep learning models are capable of extracting explanations and representations not already known to the field and act as an aid in growing our knowledge of how the human brain functions. They conclude that although more research is needed to find and address weaknesses of deep-learning models, from a mathematical point of view, its clear these models outperform standard machine learning models in many settings.

Deep learnings promise perhaps still outweighs its current usefulness to neuroimaging, but we are seeing a lot of real potential for these techniques, Plis said.

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Deep Learning Outperforms Standard Machine Learning in Biomedical Research Applications, Research Shows - Georgia State University News

Metallic alloys for aerospace components are expected to be made faster and more cheaply with the application of machine learning in Project MEDAL.

This is the aim of Project MEDAL: Machine Learning for Additive Manufacturing Experimental Design,which is being led by Intellegens, a Cambridge University spin-out specialising in artificial intelligence, the Sheffield University AMRC North West, and Boeing. It aims to accelerate the product development lifecycle of aerospace components by using a machine learning model to optimise additive manufacturing (AM) for new metal alloys.

How collaboration is driving advances in additive manufacturing

Project MEDALs research will concentrate on metal laser powder bed fusion and will focus on so-called parameter variables required to manufacture high density, high strength parts.

The project is part of the National Aerospace Technology Exploitation Programme (NATEP), a 10m initiative for UK SMEs to develop innovative aerospace technologies funded by the Department for Business, Energy and Industrial Strategy and delivered in partnership with the Aerospace Technology Institute (ATI) and Innovate UK.

In a statement, Ben Pellegrini, CEO of Intellegens, said: The intersection of machine learning, design of experiments and additive manufacturing holds enormous potential to rapidly develop and deploy custom parts not only in aerospace, as proven by the involvement of Boeing, but in medical, transport and consumer product applications.

There are many barriers to the adoption of metallic AM but by providing users, and maybe more importantly new users, with the tools they need to process a required material should not be one of them, added James Hughes, research director for Sheffield University AMRC North West. With the AMRCs knowledge in AM, and Intellegens AI tools, all the required experience and expertise is in place in order to deliver a rapid, data-driven software toolset for developing parameters for metallic AM processes to make them cheaper and faster.

Aerospace components must withstand certain loads and temperature resistances, and some materials are limited in what they can offer. There is also simultaneous push for lower weight and higher temperature resistance for better fuel efficiency, bringing new or previously impractical-to-machine metals into the aerospace sector.

One of the main drawbacks of AM is the limited material selection currently available and the design of new materials, particularly in the aerospace industry, requires expensive and extensive testing and certification cycles which can take longer than a year to complete and cost as much as 1m. Project MEDAL aims to accelerate this process.

The machine learning solution in this project can significantly reduce the need for many experimental cycles by around 80 per cent, Pellegrini said: The software platform will be able to suggest the most important experiments needed to optimise AM processing parameters, in order to manufacture parts that meet specific target properties. The platform will make the development process for AM metal alloys more time and cost-efficient. This will in turn accelerate the production of more lightweight and integrated aerospace components, leading to more efficient aircraft and improved environmental impact.

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Project MEDAL to apply machine learning to aero innovation - The Engineer

Intelligent process automation (IPA) refers to tasks that are automated or optimized in part by artificial intelligence and machine learning algorithms. IPA tools can reduce human intervention in a variety of business processes. IPA solutions go beyond simple, rule-based tasks.

Machine Learning (ML) Intelligent Process Automation Marketresearch is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. It also provides market information in terms of development and its capacities.

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Some of the important players in Machine Learning (ML) Intelligent Process Automation market are Automation Anywhere, Inc., UiPath., Blue Prism Limited., Pegasystems Inc., AntWorks, NICE Ltd., KOFAX INC., Softomotive Ltd., SAP SE, AutomationEdge, eggplant., LarcAI, Kryon Systems, Autologyx, Sanbot Innovation Technology., Ltd, Cinnamon, Inc., Wipro Limited, Xerox Corporation, Tata Consultancy Services Limited., IBM Corporation.

Machine Learning (ML) Intelligent Process Automation Market is growing at a High CAGR during the forecast period 2021-2027. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

Intelligent process automation (IPA) refers to tasks that are automated or optimized in part by artificial intelligence and machine learning algorithms. IPA tools can reduce human intervention in a variety of business processes. IPA solutions go beyond simple, rule-based tasks.

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Machine Learning (ML) Intelligent Process Automation market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Machine Learning (ML) Intelligent Process Automation markets trajectory between forecast periods.

Global Machine Learning (ML) Intelligent Process Automation Market research report offers:

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Regions Covered in the Global Machine Learning (ML) Intelligent Process Automation Market Report 2021: The Middle East and Africa(GCC Countries and Egypt) North America(the United States, Mexico, and Canada) South America(Brazil etc.) Europe(Turkey, Germany, Russia UK, Italy, France, etc.) Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

The cost analysis of the Global Machine Learning (ML) Intelligent Process Automation Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Key questions answered in the report include:

Table of Content (TOC)

Global Machine Learning (ML) Intelligent Process Automation Market Report 2021 Growth, Trend and Forecast to 2027

Chapter 1 Machine Learning (ML) Intelligent Process Automation Market Overview

Chapter 2 Global Economic Impact on Machine Learning (ML) Intelligent Process Automation Industry

Chapter 3 Global Machine Learning (ML) Intelligent Process Automation Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region (2014-2021)

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions (2014-2021)

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Machine Learning (ML) Intelligent Process Automation Market Forecast (2021-2027)

Chapter 13 Appendix

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Forecast On Machine Learning (ML) Intelligent Process Automation Market Witness the Growth of Great Billion by 2027 With Top Companies Like Automation...

Machine learning was shown to identify patients with rheumatoid arthritis (RA) who present an increased chance of achieving clinical response with sarilumab, with those selected also showing an inferior response to adalimumab, according to an abstract presented at ACR Convergence, the annual meeting of the American College of Rheumatology (ACR).

In prior phase 3 trials comparing the interleukin 6 receptor (IL-6R) inhibitor sarilumab with placebo and the tumor necrosis factor (TNF-) inhibitor adalimumab, sarilumab appeared to provide superior efficacy for patients with moderate to severe RA. Although promising, the researchers of the abstract highlight that treatment of RA requires a more individualized approach to maximize efficacy and minimize risk of adverse events.

The characteristics of patients who are most likely to benefit from sarilumab treatment remain poorly understood, noted researchers.

Seeking to better identify the patients with RA who may best benefit from sarilumab treatment, the researchers applied machine learning to select from a predefined set of patient characteristics, which they hypothesized may help delineate the patients who could benefit most from either antiIL-6R or antiTNF- treatment.

Following their extraction of data from the sarilumab clinical development program, the researchers utilized a decision tree classification approach to build predictive models on ACR response criteria at week 24 in patients from the phase 3 MOBILITY trial, focusing on the 200-mg dose of sarilumab. They incorporated the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) algorithm, including 17 categorical and 25 continuous baseline variables as candidate predictors. These included protein biomarkers, disease activity scoring, and demographic data, added the researchers.

Endpoints used were ACR20, ACR50, and ACR70 at week 24, with the resulting rule validated through application on independent data sets from the following trials:

Assessing the end points used, it was found that the most successful GUIDE model was trained against the ACR20 response. From the 42 candidate predictor variables, the combined presence of anticitrullinated protein antibodies (ACPA) and C-reactive protein >12.3 mg/L was identified as a predictor of better treatment outcomes with sarilumab, with those patients identified as rule-positive.

These rule-positive patients, which ranged from 34% to 51% in the sarilumab groups across the 4 trials, were shown to have more severe disease and poorer prognostic factors at baseline. They also exhibited better outcomes than rule-negative patients for most end points assessed, except for patients with inadequate response to TNF inhibitors.

Notably, rule-positive patients had a better response to sarilumab but an inferior response to adalimumab, except for patients of the HAQ-Disability Index minimal clinically important difference end point.

If verified in prospective studies, this rule could facilitate treatment decision-making for patients with RA, concluded the researchers.

Reference

Rehberg M, Giegerich C, Praestgaard A, et al. Identification of a rule to predict response to sarilumab in patients with rheumatoid arthritis using machine learning and clinical trial data. Presented at: ACR Convergence 2020; November 5-9, 2020. Accessed January 15, 2021. 021. Abstract 2006. https://acrabstracts.org/abstract/identification-of-a-rule-to-predict-response-to-sarilumab-in-patients-with-rheumatoid-arthritis-using-machine-learning-and-clinical-trial-data/

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Machine Learning Shown to Identify Patient Response to Sarilumab in Rheumatoid Arthritis - AJMC.com Managed Markets Network

We successfully made it through 2020 and 2021 is finally upon us. While some things, like the way businesses operate, have changed drastically, others remain the same. In the current times, companies are increasingly going online and operating with newer tech solutions to keep up with the changes that the pandemic has brought about in the market.

Companies across the world are adopting Data Science and Machine Learning to understand complex business problems, extract meaningful insights and formulate ways to resolve them. Theyre being used across several sectors and for diverse use cases. These can be anything from banking & finance departments using machine learning algorithms to identify forged signatures to supply chain and manufacturing companies using it for smarter inventory management. In the same vein, airline companies are using data science to map flight delay and develop loyalty programs, and the gaming industry is applying it to improve gaming models based on insights.

These job roles offer some of the highest salaries. Therefore, many engineering graduates in India are interested to pursue their M. tech in Data Science and Machine Learning. The salary scale in this domain ranges from Rs 4 Lakhs per annum to Rs 25 Lakhs per annum, considering various factors. In India, the average pay scale of a Data Scientist is estimated to be Rs 7 Lakhs per annum. Hence the incredible demand. Check out all the lucrative roles you can bag with these skills:

1. Data AnalystAs a data analyst, you will be responsible for various tasks, including visualisation, munging and processing of massive amounts of data. You will also have to perform queries on the databases from time to time. One of the most important skills to gain for you, as a data analyst would be optimisation. This is because you will have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.

2. Data EngineersAs a Data Engineer, you build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimised. You will also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.

3. Database AdministratorYour job profile is pretty much self-explanatory: You will be responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on your requirements. You will also be responsible for database backups and recoveries.

4. Machine Learning EngineerAs a Machine Learning Engineer, you will be in high demand today. However, the job profile comes with its challenges. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc., you would also be expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.

5. Data ScientistYou have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, you are expected to perform predictive analysis and run a fine-toothed comb through an unstructured/disorganised data to offer actionable insights. You could also do this by identifying trends and patterns that can help the companies in making better decisions.

6. Data ArchitectAs a Data Architect, you create the blueprints for data management so that the databases can be easily integrated, centralised, and protected with the best security measures. You must also ensure that the Data Engineers have the best tools and systems to work with. Some other related job roles worth mentioning include Statistician, Business analyst, Data and Analytics Manager.

For those whod love to upskill, Great Learning has emerged as one of Indias leading professional learning services with a footprint in 140 countries and has delivered 55 million+ learning hours. With a curriculum formulated by industry experts, their programs have helped learners successfully transition to new domains and grow in their fields. They offer courses on some of the hottest topics of today Data Science and Machine Learning, Artificial Intelligence etc.

Read more:

Bangalore based Great Learning can help you unleash the potential of an M-Tech in Data Science & Machine - Times of India

Sri Lanka revives port deal with India and Japan for sea terminal Business Standard First quasi-judicial body to strengthen its digital back-end

Topics CERC|artificial intelligence|machine learning

Shreya Jai | New Delhi Last Updated at January 15, 2021 06:10 IST

The apex power sector regulator, the Central Electricity Regulatory Commission (CERC), is planning to set up an artificial intelligence (AI)-based regulatory expert system tool (REST) for improving access to information and assist the commission in discharge of its duties. So far, only the Supreme Court (SC) has an electronic filing (e-filing) system and is in the process of building an AI-based back-end service.

The CERC will be the first such quasi-judicial regulatory body to embrace AI and machine learning (ML). The decision comes at a time when the CERC has been shut for four ...

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First Published: Fri, January 15 2021. 06:10 IST

Read the original:

CERC plans to embrace AI, machine learning to improve functioning - Business Standard