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The enrollment of patients who are unlikely to show meaningful cognitive decline with placebo may make it more difficult to show the benefits of active treatment for cognition. Recent research used data from the placebo arm of 5 phase 3 trials, showing that predictive machine learning models can potentially increase sensitivity to effects from treatment and reduce the requirements for sample size in clinical trials.1

In total, 1982 patients were included in the pooled placebo analysis, with meaningful cognitive decline not observed in 42% to 58% of individuals at the end of trials. Using the predictive machine learning models, positive predictive values were approximately 12% to 25% higher than the sample rate of meaningful cognitive decline. Notably, negative predictive values of models were approximately 15% to 24% higher than the base rate of patients who had stable cognition at the end of trial.

Ali Ezzati, MD, assistant professor, department of neurology, at the Albert Einstein College of Medicine and Montefiore Medical Center, presented this study during the experimental therapeutics in dementia session at the 2023 American Academy of Neurology (AAN) Annual Meeting, April 22-27, in Boston, Massachusetts. During the meeting, Ezzati sat down with NeurologyLive in an interview to talk about the reason behind the difficulties and failures in clinical trials for Alzheimer disease (AD). He also spoke about the findings from his study that were presented, and the proposal to improve the design of trials using machine learning predictive models.

Click here for more coverage on AAN 2023.

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Enhancing Alzheimer Disease Clinical Trials With Predictive ... - Neurology Live

PRESS RELEASE

Published May 12, 2023

The recent analysis by Quadintel on the Middle East and Africa Machine Learning Market Report 2023 revolves around various aspects of the market, including characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends, strategies, etc. It also includes COVID-19 Outbreak Impact, accompanied by traces of the historic events. The study highlights the list of projected opportunities, sales and revenue on the basis of region and segments. Apart from that, it also documents other topics such as manufacturing cost analysis, Industrial Chain, etc. For better demonstration, it throws light on the precisely obtained data with the thoroughly crafted graphs, tables, Bar & Pie Charts, etc.

Get a report on Middle East and Africa Machine Learning Market (Including Full TOC, 100+ Tables & Figures, and charts). Covers Precise Information on Pre & Post COVID-19 Market Outbreak by Region

Request to Download Free Sample Copy of Middle East and Africa Machine Learning Market Report @https://www.quadintel.com/request-sample/middle-east-and-africa-machine-learning-market/QI042

The market for machine learning in the Middle East and Africa is rapidly growing and expected to reach a value of USD 0.50 billion by 2023, with a compound annual growth rate of 29.1% from 2018-2023.Machine learning has become increasingly important due to the availability of data and the need to process it for meaningful insights.The market can be segmented based on components, service, organization size, and application.

The use of machine learning in healthcare has become popular in the Middle East as hospitals are using this technology to make precise diagnoses, prevent diseases, and provide treatment to individuals. The adoption of machine learning in retail and healthcare industries to provide better consumer experiences and increase automation is driving the market growth.

The slow adoption of machine learning in Africa can be attributed to the lack of adequate infrastructure and consumer spending power. Also, the unavailability of skilled cohorts with adequate machine learning skills is a significant barrier to further development in the market.

The key players in the market are Google Inc., Microsoft, IBM Watson, Amazon, and Intel. These companies are investing heavily in the development of machine learning technologies and are driving the growth of the market.

The report provides an overview of the market, market drivers, and challenges, historical, current and forecasted market size data, analysis of the competitive landscape, and profiles of major competitors. The report also provides insights into the value chain, new technology innovations, government guidelines, export and import analysis, and growth strategies taken by major companies in the market.

The market for machine learning in the Middle East and Africa is rapidly growing due to increased data availability, the need for meaningful insights, and the adoption of machine learning in various industries. The key players in the market are investing heavily in developing machine learning technologies, and the market is expected to continue growing in the future.

Download Free Sample Copy of Middle East and Africa Machine Learning Market Report @https://www.quadintel.com/request-sample/middle-east-and-africa-machine-learning-market/QI042

Our tailormade report can help companies and investors make efficient strategic moves by exploring the crucial information on market size, business trends, industry structure, market share, and market predictions.

Apart from the general projections, our report outstands as it includes thoroughly studied variables, such as the COVID-19 containment status, the recovery of the end-use market, and the recovery timeline for 2020/ 2021

Analysis on COVID-19 Outbreak Impact Include:In light of COVID-19, the report includes a range of factors that impacted the market. It also discusses the trends. Based on the upstream and downstream markets, the report precisely covers all factors, including an analysis of the supply chain, consumer behavior, demand, etc. Our report also describes how vigorously COVID-19 has affected diverse regions and significant nations.

Report Include:

For more information or any query mail at [emailprotected]

Each report by the Quadintel contains more than 100+ pages, specifically crafted with precise tables, charts, and engaging narrative: The tailor-made reports deliver vast information on the market with high accuracy. The report encompasses: Micro and macro analysis, Competitive landscape, Regional dynamics, Operational landscape, Legal Set-up, and Regulatory frameworks, Market Sizing and Structuring, Profitability and Cost analysis, Demographic profiling and Addressable market, Existing marketing strategies in the market, Segmentation analysis of Market, Best practice, GAP analysis, Leading market players, Benchmarking, Future market trends and opportunities.

Geographical Breakdown:The regional section of the report analyses the market on the basis of region and national breakdowns, which includes size estimations, and accurate data on previous and future growth. It also mentions the effects and the estimated course of Covid-19 recovery for all geographical areas. The report gives the outlook of the emerging market trends and the factors driving the growth of the dominating region to give readers an outlook of prevailing trends and help in decision making.

Nations:Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, New Zealand, Nigeria, Norway, Peru, Philippines, Poland, Portugal, Romania, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, UAE, UK, USA, Venezuela, Vietnam

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Thoroughly Described Qualitative COVID 19 Outbreak Impact Include Identification and Investigation on:Market Structure, Growth Drivers, Restraints and Challenges, Emerging Product Trends & Market Opportunities, Porters Fiver Forces. The report also inspects the financial standing of the leading companies, which includes gross profit, revenue generation, sales volume, sales revenue, manufacturing cost, individual growth rate, and other financial ratios. The report basically gives information about the Market trends, growth factors, limitations, opportunities, challenges, future forecasts, and information on the prominent and other key market players.

Key questions answered:This study documents the affect ofCOVID 19 Outbreak: Our professionally crafted report contains precise responses and pinpoints the excellent opportunities for investors to make new investments. It also suggests superior market plan trajectories along with a comprehensive analysis of current market infrastructures, prevailing challenges, opportunities, etc. To help companies design their superior strategies, this report mentions information about end-consumer target groups and their potential operational volumes, along with the potential regions and segments to target and the benefits and limitations of contributing to the market. Any markets robust growth is derived by its driving forces, challenges, key suppliers, key industry trends, etc., which is thoroughly covered in our report. Apart from that, the accuracy of the data can be specified by the effective SWOT analysis incorporated in the study.

A section of the report is dedicated to the details related to import and export, key players, production, and revenue, on the basis of the regional markets. The report is wrapped with information about key manufacturers, key market segments, the scope of products, years considered, and study objectives.

It also guides readers through segmentation analysis based on product type, application, end-users, etc. Apart from that, the study encompasses a SWOT analysis of each player along with their product offerings, production, value, capacity, etc.

List of Factors Covered in the Report are:Major Strategic Developments: The report abides by quality and quantity. It covers the major strategic market developments, including R&D, M&A, agreements, new products launch, collaborations, partnerships, joint ventures, and geographical expansion, accompanied by a list of the prominent industry players thriving in the market on a national and international level.

Key Market Features:Major subjects like revenue, capacity, price, rate, production rate, gross production, capacity utilization, consumption, cost, CAGR, import/export, supply/demand, market share, and gross margin are all assessed in the research and mentioned in the study. It also documents a thorough analysis of the most important market factors and their most recent developments, combined with the pertinent market segments and sub-segments.

Request a Sample PDF copy of this report @https://www.quadintel.com/request-sample/middle-east-and-africa-machine-learning-market/QI042

List of Highlights & ApproachThe report is made using a variety of efficient analytical methodologies that offers readers an in-depth research and evaluation on the leading market players and comprehensive insight on what place they are holding within the industry. Analytical techniques, such as Porters five forces analysis, feasibility studies, SWOT analyses, and ROI analyses, are put to use to examine the development of the major market players.

Points Covered in Middle East and Africa Machine Learning Market Report:

Middle East and Africa Machine Learning Market Research Report

Section 1: Middle East and Africa Machine Learning Market Industry Overview

Section 2: Economic Impact on Middle East and Africa Machine Learning

Section 3: Market Competition by Industry Producers

Section 4: Productions, Revenue (Value), according to regions

Section 5: Supplies (Production), Consumption, Export, Import, geographically

Section 6: Productions, Revenue (Value), Price Trend, Product Type

Section 7: Market Analysis, on the basis of Application

Section 8: Middle East and Africa Machine Learning Market Pricing Analysis

Section 9: Market Chain, Sourcing Strategy, and Downstream Buyers

Section 10: Strategies and key policies by Distributors/Suppliers/Traders

Section 11: Key Marketing Strategy Analysis, by Market Vendors

Section 12: Market Effect Factors Analysis

Section 13: Middle East and Africa Machine Learning Market Forecast

..and view more in complete table of Contents

Thank you for reading; we also provide a chapter-by-chapter report or a report based on region, such as North America, Europe, or Asia.

Request Full Report:https://www.quadintel.com/request-sample/middle-east-and-africa-machine-learning-market/QI042

About Quadintel:

We are the best market research reports provider in the industry. Quadintel believes in providing quality reports to clients to meet the top line and bottom-line goals which will boost your market share in todays competitive environment. Quadintel is a one-stop solution for individuals, organizations, and industries that are looking for innovative market research reports.

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Middle East and Africa Machine Learning Market Spurs as Demand ... - Digital Journal

SAS is an organization dedicated to responsible innovation and using technology to ignite positive change. In line with its mission, SAS will apply crowd-driven artificial intelligence (AI) and machine learning to help protect endangered sea turtles. SAS is working with the UNC Center for Galapagos Studies (CGS) on this project and to further research in several initiatives on the Islands in general.

According to a release from SAS,1 an app called ConserVision, will allow citizen scientists to match images of turtles' facial markings to help train a SAS computer vision model. Once the model can accurately identify turtles individually, researchers will have valuable information more quickly to better track each turtle's health and migratory patterns over periods of time. The ultimate goal from there is to allow the model to perform facial recognition on any sea turtle image, whether it comes from a conservation group or a vacationing tourist.

SAS also eventually aims to have the app identify a health index regarding growth rates, health threats, and presence data. From there, researchers can better understand temporal and spatial movement patterns of these turtles and to identify health risks due to marine debris, boat strikes, diseases, etc.

"As our challenges as a global community get increasingly more complex, we need dynamic ways to access and use information to ramp up conservation efforts," said Sarah Hiser, MSc, principal technical architect at SAS, said in the release. "By using technology like analytics, AI and machine learning to quantify the natural world, we gain knowledge to help protect ecosystems and tackle climate change."1

"For over 10 years, the Galapagos Science Center has hosted exceptional scientists doing innovative research that increases our understanding of the environment and results in positive real-world outcomes," explained UNC-Chapel Hill interim vice chancellor for research, Penny Gordon-Larsen, PhD, in the release. "This innovative public-private partnership with SAS will enhance the center's capacity for analyzing data that will positively impact both the environment and the people who inhabit these magnificent islands."1

SAS will help UNC CGS with 3 projects focusing on marine life, including:

Reference

SAS seeks crowd-driven AI to protect endangered sea turtles in Galapagos. News release. SAS. Published May 9, 2023. Accessed May 12, 2023. https://prnmedia.prnewswire.com/news-releases/sas-seeks-crowd-driven-ai-to-protect-endangered-sea-turtles-in-galapagos-301819633.html

Read more from the original source:
Artificial intelligence used to protect sea turtles in the Galapagos - DVM 360

Scientists at Northwestern University Feinberg School of Medicine have discovered that unresolved secondary bacterial pneumonia is a key driver of death in patients with COVID-19, affecting nearly half of the patients who required mechanical ventilation support. Their findings, published in The Journal of Clinical Investigation, also debunk the theory that COVID-19 causes a cytokine storm leading to death.

Machine learning finds no evidence of cytokine storm in critically ill patients with COVID-19.

Secondary bacterial infection of the lung (pneumonia) was extremely common in patients with COVID-19, affecting almost half the patients who required support from mechanical ventilation. By applying machine learning to medical record data, scientists at Northwestern University Feinberg School of Medicine have found that secondary bacterial pneumonia that does not resolve was a key driver of death in patients with COVID-19, results published in The Journal of Clinical Investigation.

Bacterial infections may even exceed death rates from the viral infection itself, according to the findings. The scientists also found evidence that COVID-19 does not cause a cytokine storm, so often believed to cause death.

Benjamin Singer, MD, the Lawrence Hicks Professor of Pulmonary Medicine in the Department of Medicine and a Northwestern Medicine pulmonary and critical care physician. Credit: Northwestern Medicine

Our study highlights the importance of preventing, looking for, and aggressively treating secondary bacterial pneumonia in critically ill patients with severe pneumonia, including those with COVID-19, said senior author Benjamin Singer, MD, the Lawrence Hicks Professor of Pulmonary Medicine in the Department of Medicine and a Northwestern Medicine pulmonary and critical care physician.

The investigators found nearly half of patients with COVID-19 develop a secondary ventilator-associated bacterial pneumonia.

Those who were cured of their secondary pneumonia were likely to live, while those whose pneumonia did not resolve were more likely to die, Singer said. Our data suggested that the mortality related to the virus itself is relatively low, but other things that happen during the ICU stay, like secondary bacterial pneumonia, offset that.

The study findings also negate the cytokine storm theory, said Singer, also a professor of Biochemistry and Molecular Genetics.

The term cytokine storm means an overwhelming inflammation that drives organ failure in your lungs, your kidneys, your brain and other organs, Singer said. If that were true, if cytokine storm were underlying the long length of stay we see in patients with COVID-19, we would expect to see frequent transitions to states that are characterized by multi-organ failure. Thats not what we saw.

The study analyzed 585 patients in the intensive care unit (ICU) at Northwestern Memorial Hospital with severe pneumonia and respiratory failure, 190 of whom had COVID-19. The scientists developed a new machine learning approach called CarpeDiem, which groups similar ICU patient-days into clinical states based on electronic health record data. This novel approach, which is based on the concept of daily rounds by the ICU team, allowed them to ask how complications like bacterial pneumonia impacted the course of the illness.

These patients or their surrogates consented to enroll in the Successful Clinical Response to Pneumonia Therapy (SCRIPT) study, an observational trial to identify new biomarkers and therapies for patients with severe pneumonia. As part of SCRIPT, an expert panel of ICU physicians used state-of-the-art analysis of lung samples collected as part of clinical care to diagnose and adjudicate the outcomes of secondary pneumonia events.

The application of machine learning and artificial intelligence to clinical data can be used to develop better ways to treat diseases like COVID-19 and to assist ICU physicians managing these patients, said study co-first author Catherine Gao, MD, an instructor in the Department of Medicine, Division of Pulmonary and Critical Care and a Northwestern Medicine physician.

The importance of bacterial superinfection of the lung as a contributor to death in patients with COVID-19 has been underappreciated, because most centers have not looked for it or only look at outcomes in terms of presence or absence of bacterial superinfection, not whether treatment is successful or not, said study co-author Richard Wunderink, MD, who leads the Successful Clinical Response in Pneumonia Therapy Systems Biology Center at Northwestern.

The next step in the research will be to use molecular data from the study samples and integrate it with machine learning approaches to understand why some patients go on to be cured of pneumonia and some dont. Investigators also want to expand the technique to larger datasets and use the model to make predictions that can be brought back to the bedside to improve the care of critically ill patients.

Reference: Machine learning links unresolving secondary pneumonia to mortality in patients with severe pneumonia, including COVID-19 by Catherine A. Gao, Nikolay S. Markov, Thomas Stoeger, Anna E. Pawlowski, Mengjia Kang, Prasanth Nannapaneni, Rogan A. Grant, Chiagozie Pickens, James M. Walter, Jacqueline M. Kruser, Luke V. Rasmussen, Daniel Schneider, Justin Starren, Helen K. Donnelly, Alvaro Donayre, Yuan Luo, G.R. Scott Budinger, Richard G. Wunderink, Alexander V. Misharin and Benjamin D. Singer, 27 April 2023, The Journal of Clinical Investigation.DOI: 10.1172/JCI170682

Other Northwestern authors on the paper includeNikolay Markov;Thomas Stoeger, PhD;Anna Pawlowski;Mengjia Kang, MS;Prasanth Nannapaneni;Rogan Grant;Chiagozie Pickens 14 MD 17 GME, assistant professor of Medicine in the Division of Pulmonary and Critical Care;James Walter, MD, assistant professor of Medicine in the Division of Pulmonary and Critical Care; Jacqueline Kruser, MD;Luke Rasmussen, MS;Daniel Schneider, MS;Justin Starren, MD, PhD, chief of Health and Biomedical Informatics in the Department of Preventive Medicine;Helen Donnelly;Alvaro Donayre; Yuan Luo, PhD, director of the Center for Collaborative AI in Healthcare and associate professor of Preventive Medicine;Scott Budinger, MD, chief of Pulmonary and Critical Care in the Department of Medicine; andAlexander Misharin, MD, PhD, associate professor of Medicine in the Division of Pulmonary and Critical Care.

The study was supported bythe Simpson Querrey Lung Institute for Translational Sciences and grantU19AI135964 from theNational Institute of Allergy and Infectious Diseasesof the National Institutes of Health.

Read more:
Cytokine Storm Debunked: Machine Learning Exposes the True Killer of COVID-19 Patients - SciTechDaily

Artificial intelligence (AI)

Scientists are warning machine learning will soon outsmart humans maybe its time for us to take note

Last Monday an eminent, elderly British scientist lobbed a grenade into the febrile anthill of researchers and corporations currently obsessed with artificial intelligence or AI (aka, for the most part, a technology called machine learning). The scientist was Geoffrey Hinton, and the bombshell was the news that he was leaving Google, where he had been doing great work on machine learning for the last 10 years, because he wanted to be free to express his fears about where the technology he had played a seminal role in founding was heading.

To say that this was big news would be an epic understatement. The tech industry is a huge, excitable beast that is occasionally prone to outbreaks of irrational exuberance, ie madness. One recent bout of it involved cryptocurrencies and a vision of the future of the internet called Web3, which an astute young blogger and critic, Molly White, memorably describes as an enormous grift thats pouring lighter fluid on our already smoldering planet.

We are currently in the grip of another outbreak of exuberance triggered by Generative AI chatbots, large language models (LLMs) and other exotic artefacts enabled by massive deployment of machine learning which the industry now regards as the future for which it is busily tooling up.

Recently, more than 27,000 people including many who are knowledgeable about the technology became so alarmed about the Gadarene rush under way towards a machine-driven dystopia that they issued an open letter calling for a six-month pause in the development of the technology. Advanced AI could represent a profound change in the history of life on Earth, it said, and should be planned for and managed with commensurate care and resources.

It was a sweet letter, reminiscent of my morning sermon to our cats that they should be kind to small mammals and garden birds. The tech giants, which have a long history of being indifferent to the needs of society, have sniffed a new opportunity for world domination and are not going to let a group of nervous intellectuals stand in their way.

Which is why Hintons intervention was so significant. For he is the guy whose research unlocked the technology that is now loose in the world, for good or ill. And thats a pretty compelling reason to sit up and pay attention.

He is a truly remarkable figure. If there is such a thing as an intellectual pedigree, then Hinton is a thoroughbred.

His father, an entomologist, was a fellow of the Royal Society. His great-great-grandfather was George Boole, the 19th-century mathematician who invented the logic that underpins all digital computing.

His great-grandfather was Charles Howard Hinton, the mathematician and writer whose idea of a fourth dimension became a staple of science fiction and wound up in the Marvel superhero movies of the 2010s. And his cousin, the nuclear physicist Joan Hinton, was one of the few women to work on the wartime Manhattan Project in Los Alamos, which produced the first atomic bomb.

Hinton has been obsessed with artificial intelligence for all his adult life, and particularly in the problem of how to build machines that can learn. An early approach to this was to create a Perceptron a machine that was modelled on the human brain and based on a simplified model of a biological neuron. In 1958 a Cornell professor, Frank Rosenblatt, actually built such a thing, and for a time neural networks were a hot topic in the field.

But in 1969 a devastating critique by two MIT scholars, Marvin Minsky and Seymour Papert, was published and suddenly neural networks became yesterdays story.

Except that one dogged researcher Hinton was convinced that they held the key to machine learning. As New York Times technology reporter Cade Metz puts it, Hinton remained one of the few who believed it would one day fulfil its promise, delivering machines that could not only recognise objects but identify spoken words, understand natural language, carry on a conversation, and maybe even solve problems humans couldnt solve on their own.

In 1986, he and two of his colleagues at the University of Toronto published a landmark paper showing that they had cracked the problem of enabling a neural network to become a constantly improving learner using a mathematical technique called back propagation. And, in a canny move, Hinton christened this approach deep learning, a catchy phrase that journalists could latch on to. (They responded by describing him as the godfather of AI, which is crass even by tabloid standards.)

In 2012, Google paid $44m for the fledgling company he had set up with his colleagues, and Hinton went to work for the technology giant, in the process leading and inspiring a group of researchers doing much of the subsequent path-breaking work that the company has done on machine learning in its internal Google Brain group.

During his time at Google, Hinton was fairly non-committal (at least in public) about the danger that the technology could lead us into a dystopian future. Until very recently, he said, I thought this existential crisis was a long way off. So, I dont really have any regrets over what I did.

But now that he has become a free man again, as it were, hes clearly more worried. In an interview last week, he started to spell out why. At the core of his concern was the fact that the new machines were much better and faster learners than humans. Back propagation may be a much better learning algorithm than what weve got. Thats scary We have digital computers that can learn more things more quickly and they can instantly teach it to each other. Its like if people in the room could instantly transfer into my head what they have in theirs.

Whats even more interesting, though, is the hint that whats really worrying him is the fact that this powerful technology is entirely in the hands of a few huge corporations.

Until last year, Hinton told Metz, the Times journalist who has profiled him, Google acted as a proper steward for the technology, careful not to release something that might cause harm.

But now that Microsoft has augmented its Bing search engine with a chatbot challenging Googles core business Google is racing to deploy the same kind of technology. The tech giants are locked in a competition that might be impossible to stop.

Hes right. Were moving into uncharted territory.

Well, not entirely uncharted. As I read of Hintons move on Monday, what came instantly to mind was a story Richard Rhodes tells in his monumental history The Making of the Atomic Bomb. On 12 September, 1933, the great Hungarian theoretical physicist Leo Szilard was waiting to cross the road at a junction near the British Museum. He had just been reading a report of a speech given the previous day by Ernest Rutherford, in which the great physicist had said that anyone who looked for a source of power in the transformation of the atom was talking moonshine.

Szilard suddenly had the idea of a nuclear chain reaction and realised that Rutherford was wrong. As he crossed the street, Rhodes writes, time cracked open before him and he saw a way to the future, death into the world and all our woe, the shape of things to come.

Szilard was the co-author (with Albert Einstein) of the letter to President Roosevelt (about the risk that Hitler might build an atomic bomb) that led to the Manhattan Project, and everything that followed.

John Naughton is an Observer columnist and chairs the advisory board of the Minderoo Centre for Technology and Democracy at Cambridge University.

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A race it might be impossible to stop: how worried should we be about AI? - The Guardian