Category Archives: Machine Learning

Machine Learning in Medicine Market Comprehensive Study is an expert and top to bottom investigation on the momentum condition of the worldwide Machine Learning in Medicine industry with an attention on the Global market. The report gives key insights available status of the Machine Learning in Medicine producers and is an important wellspring of direction and course for organizations and people keen on the business. By and large, the report gives an inside and out understanding of 2021-2028 worldwide Machine Learning in Medicine Market covering extremely significant parameters.

Free Sample Report @:

https://www.marketresearchinc.com/request-sample.php?id=28540

Key Players in This Report Include,:GoogleBio BeatsJvionLumiataDreaMedHealintArterysAtomwiseHealth FidelityGinger

(Market Size & Forecast, Different Demand Market by Region, Main Consumer Profile etcBrief Summary of Machine Learning in Medicine:

Machine Learning in Medicine Helps in Avoiding Delays in Processing, Turn-Around-Time, & Redundant Operational Costs. It is Efficient in the Management of Entire Claim Administrative Processes, Such as Adjudication, Pricing, Authorizations, & Analytics. It Provides Real-Time Claim Processing With No Wait Time for Batch Processes

Market Drivers The Rise in the Number ofPatients Opting For Medical Insurance & Increasein Premium Costs The Surge in the Geriatric Population with Chronic Diseases

Market Trend Growth in the Health Insurance Claims

Restraints High Cost Linked With Machine Learning in Medicine

The Global Machine Learning in Medicine Market segments and Market Data Break Down are illuminated below:by Type (Integrated Solutions, Standalone Solutions), Application (Healthcare Payers, Healthcare Providers, Other), Delivery Mode (On-Premise, Cloud-Based), Component (Software, Services)

This research report represents a 360-degree overview of the competitive landscape of the Global Machine Learning in Medicine Market. Furthermore, it offers massive data relating to recent trends, technological, advancements, tools, and methodologies. The research report analyzes the Global Machine Learning in Medicine Market in a detailed and concise manner for better insights into the businesses.

Regions Covered in the Machine Learning in Medicine Market: TheMiddle EastandAfrica(South Africa,Saudi Arabia,UAE,Israel,Egypt, etc.)North America(United States,Mexico&Canada)South America(Brazil,Venezuela,Argentina,Ecuador,Peru,Colombia, etc.)Europe(Turkey,Spain,Turkey, Netherlands Denmark,Belgium,Switzerland,Germany, RussiaUK,Italy,France, etc.)Asia-Pacific(Taiwan,Hong Kong,Singapore,Vietnam,China,Malaysia,Japan,Philippines,Korea,Thailand,India,Indonesia, andAustralia).

Get Upto 40% Discount on The Report @https://www.marketresearchinc.com/ask-for-discount.php?id=28540

The research study has taken the help of graphical presentation techniques such as infographics, charts, tables, and pictures. It provides guidelines for both established players and new entrants in the Global Machine Learning in Medicine Market.

The detailed elaboration of the Global Machine Learning in Medicine Market has been provided by applying industry analysis techniques such as SWOT and Porters five-technique. Collectively, this research report offers a reliable evaluation of the global market to present the overall framework of businesses.

Attractions of the Machine Learning in Medicine Market Report: The report provides granular level information about the market size, regional market share, historic market (2014-2018) and forecast (2021-2028) The report covers in-detail insights about the competitors overview, company share analysis, key market developments, and their key strategies The report outlines drivers, restraints, unmet needs, and trends that are currently affecting the market The report tracks recent innovations, key developments and start-ups details that are actively working in the market The report provides plethora of information about market entry strategies, regulatory framework and reimbursement scenario

Enquire for customization in Report @https://www.marketresearchinc.com/enquiry-before-buying.php?id=28540

Key Points Covered in the Table of Content:Chapter 1 to explain Introduction, market review, market risk and opportunities, market driving force, product scope of Machine Learning in Medicine Market;Chapter 2 to inspect the leading manufacturers (Cost Structure, Raw Material) with sales Analysis, revenue Analysis, and price Analysis of Machine Learning in Medicine Market;Chapter 3 to show the focused circumstance among the best producers, with deals, income, and Machine Learning in Medicine market share 2021;Chapter 4 to display the regional analysis of Global Machine Learning in Medicine Market with revenue and sales of an industry, from 2021to 2028;Chapter 5, 6, 7 to analyze the key countries (United States,China,Europe,Japan,Korea&Taiwan), with sales, revenue and market share in key regions;Chapter 8 and 9 to exhibit International and Regional Marketing Type Analysis, Supply Chain Analysis, Trade Type Analysis;Chapter 10 and 11 to analyze the market by product type and application/end users (industry sales, share, and growth rate) from2021 to 2028Chapter 12 to show Machine Learning in Medicine Market forecast by regions, forecast by type and forecast by application with revenue and sales, from 2021 to 2028;Chapter 13, 14 & 15 to specify Research Findings and Conclusion, Appendix, methodology and data source of Machine Learning in Medicine market buyers, merchants, dealers, sales channel.

Browse for Full Report at @:

Machine Learning in Medicine Market research provides answers to the following key questions:What is the expected growth rate of the Machine Learning in Medicine Market?What will be the Machine Learning in Medicine Market size for the forecast period, 2021 2028?What are the main driving forces responsible for changing the Machine Learning in Medicine Market trajectory?Who are the big suppliers that dominate the Machine Learning in Medicine Market across different regions? Which are their wins to stay ahead in the competition?What are the Machine Learning in Medicine Market trends business owners can rely upon in the coming years?What are the threats and challenges expected to restrict the progress of the Machine Learning in Medicine Market across different countries?

About Us

Market Research Inc is farsighted in its view and covers massive ground in global research. Local or global, we keep a close check on both markets. Trends and concurrent assessments sometimes overlap and influence the other. When we say market intelligence, we mean a deep and well-informed insight into your products, market, marketing, competitors, and customers. Market research companies are leading the way in nurturing global thought leadership. We help your product/service become the best they can with our informed approach.

Contact Us

Market Research Inc

Kevin

51 Yerba Buena Lane, Ground Suite,

Inner Sunset San Francisco, CA 94103, USA

Call Us: +1 (628) 225-1818

Write Us@sales@marketresearchinc.com

https://www.marketresearchinc.com

Read more here:
Machine Learning in Medicine Market 2021 to Perceive Biggest Trend and Opportunity by 2028 KSU | The Sentinel Newspaper - KSU | The Sentinel...

For the past few years, JPMorgan has been busy building out its machine learning capability underDaryush Laqab, its San Francisco-based head of AI platform product management, who was hired from Google in 2019. Last time we looked, the bank seemed to be paying salaries of $160-$170k to new joiners onLaqab's team.

If that sounds appealing, you might want to watch the video below so that you know what you're getting into. Recorded at the AWS re:Invent conferencein December, it's only just made it to you YouTube. The video is flagged as a day in the life of JPMorgan's machine learning data scientists, butLaqab arguably does a better of job of highlighting some of the constraints data professionals at allbanks have to work under.

"There are some barriers to smooth data science at JPMorgan," he explains - a bank is not the same as a large technology firm.

For example, data scientists at JPMorgan have to check data is authorized for use, saysLaqab: "They need to go to a process to log that use and make surethat they have the adequate approvals for that intent in terms of use."

They also have to deal with the legacy infrastructureissue: "We are a large organization, we have a lot of legacy infrastructure," says Laqab. "Like any other legacy infrastructure, it is built over time,it is patched over time. These are tightly integrated,so moving part or all of that infrastructure to public cloud,replacing rule base engines with AI/ML based engines.All of that takes time and brings inertia to the innovation."

JPMorgan's size and complexity is another source of inertia as multiple business lines in multiple regulated entities in different regulated environments need to be considered. "Making sure that those regulatory obligationsare taken care of, again, slows down data science at times," saysLaqab.

And then there are more specific regulations such as those concerning model governance. At JPMorgan, a machine learning model can't go straight into a production environment."It needs to go through a model review and a model governance process," says Laqab. "- To make sure we have another set of eyes that looksat how that model was created, how that model was developed..." And then there are software governance issues too.

Despite all these hindrances, JPMorgan has already productionized AI models and built an 'Omni AI ecosystem,'which Laqab heads,to help employees to identify and ingest minimum viable data so that they canbuild models faster. Laqab saysthe bank saved $150m in expenses in 2019 as a result. JPMorgan's AI researchers are now working on everything fromFAQ bots and chat bots, to NLP search models for the bank'sown content, pattern recognition in equities markets and email processing. - The breadth of work on offer is considerable. "We play in every market that is out there," saysLaqab,

The bank has also learned that the best way to structure its AI team is to split people into data scientists who train and create models and machine learning engineers who operationalize models, saysLaqab. - Before you apply, you might want to consider which you'd rather be.

Photo by NeONBRAND on Unsplash

Have a confidential story, tip, or comment youd like to share? Contact:sbutcher@efinancialcareers.comin the first instance. Whatsapp/Signal/Telegram also available. Bear with us if you leave a comment at the bottom of this article: all our comments are moderated by human beings. Sometimes these humans might be asleep, or away from their desks, so it may take a while for your comment to appear. Eventually it will unless its offensive or libelous (in which case it wont.)

Read this article:
The head of JPMorgan's machine learning platform explained what it's like to work there - eFinancialCareers

It is common for patients with psychosis or depression to experience symptoms of both conditions which has meant that traditionally, mental health diagnoses have been given for a primary illness with secondary symptoms of the other.

Making an accurate diagnosis often poses difficulties to mental health clinicians and diagnoses often do not accurately reflect the complexity of individual experience or neurobiology. For example, a patient being diagnosed with psychosis will often have depression regarded as a secondary condition, with more focus on the psychosis symptoms, such as hallucinations or delusions; this has implications on treatment decisions for patients.

A team at the University of Birminghams Institute for Mental Health and Centre for Human Brain Health, along with researchers at the European Union-funded PRONIA consortium, explored the possibility of using machine learning to create extremely accurate models of pure forms of both illnesses and using these models to investigate the diagnostic accuracy of a cohort of patients with mixed symptoms. The results of this study have been published in Schizophrenia Bulletin.

Paris Alexandros Lalousis, lead author, explains that the majority of patients have co-morbidities, so people with psychosis also have depressive symptoms and vice versa That presents a big challenge for clinicians in terms of diagnosing and then delivering treatments that are designed for patients without co-morbidity. Its not that patients are misdiagnosed, but the current diagnostic categories we have do not accurately reflect the clinical and neurobiological reality.

The researchers analysed questionnaire responses and detailed clinical interviews, as well as data from structural magnetic resonance imaging from a cohort of 300 patients taking part in the study. From this group of patients, they identified small subgroups of patients, who could be classified as suffering either from psychosis without any symptoms of depression, or from depression without any psychotic symptoms.

With the goal of developing a precise disease profile for each patient and testing it against their diagnosis to see how accurate it was, the research team was able to identify machine learning models of pure depression, and pure psychosis by using the collected data. They were then able to use machine learning methods to apply these models to patients with symptoms of both illnesses.

The team discovered that patients with depression as a primary illness were more likely to have accurate mental health diagnoses, whereas patients with psychosis with depression had symptoms which most frequently leaned towards the depression dimension. This may suggest that depression plays a greater part in the illness than had previously been thought.

Lalousis added: There is a pressing need for better treatments for psychosis and depression, conditions which constitute a major mental health challenge worldwide. Our study highlights the need for clinicians to understand better the complex neurobiology of these conditions, and the role of co-morbid symptoms; in particular considering carefully the role that depression is playing in the illness.

In this study we have shown how using sophisticated machine learning algorithms, which take into account clinical, neurocognitive, and neurobiological factors can aid our understanding of the complexity of mental illness. In the future, we think machine learning could become a critical tool for accurate diagnosis. We have a real opportunity to develop data-driven diagnostic methods this is an area in which mental health is keeping pace with physical health and its really important that we keep up that momentum.

Continued here:
Mental health diagnoses and the role of machine learning - Health Europa

Reading Time: 4 minutes

By Emily Newton

The Internet of Things (IoT) and machine learning are two of the most disruptive technologies in business today. Separately, both of these innovations can bring remarkable benefits to any company. Together, they can transform your business entirely.

The intersection of IoT devices and machine learning is a natural progression. Machine learning needs large pools of relevant data to work at its best, and the IoT can supply it. As adoption of both soars, companies should start using them in conjunction.

Here are five ways the IoT and machine learning can improve operations in any business.

Around 25% of businesses today use IoT devices, and this figure will keep climbing. As companies implement more of these sensors, they add places where they can gather data. Machine learning algorithms can then analyze this data to find inefficiencies in the workplace.

Looking at various workplace data, a machine learning program could see where a company spends an unusually high amount of time. It could then suggest a new workflow that would reduce the effort employees expend in that area. Business leaders may not have ever realized this was a problem area without machine learning.

Machine learning programs are skilled at making connections between data points that humans may miss. They can also make predictions 20 times earlier than traditional tools and do so with more accuracy. With IoT devices feeding them more data, theyll only become faster and more accurate.

Machine learning and the IoT can also automate routine tasks. Business process automation (BPA) leverages AI to handle a range of administrative tasks, so workers dont have to. As IoT devices feed more data into these programs, they become even more effective.

Over time, technology like this has contributed to a 40% productivity increase in some industries. Automating and streamlining tasks like scheduling and record-keeping frees employees to focus on other, value-adding work. BPAs potential doesnt stop there, either.

BPA can automate more than straightforward data manipulation tasks. It can talk to customers, plan and schedule events, run marketing campaigns and more. With more comprehensive IoT implementation, it would have access to more areas, becoming even more versatile.

One of the most promising areas for IoT implementation is in the supply chain. IoT sensors in vehicles or shipping containers can provide companies with critical information like real-time location data or product quality. This data alone improves supply chain visibility, but paired with machine learning, it could transform your business.

Machine learning programs can take this real-time data from IoT sensors and put it into action. It could predict possible disruptions and warn workers so they can respond accordingly. These predictive analytics could save companies the all-too-familiar headache of supply chain delays.

UPS Orion tool is the gold standard for what machine learning can do for supply chains. The system has saved the shipping giant 10 million gallons of fuel a year by adjusting routes on the fly based on traffic and weather data.

If a company cant understand the vulnerabilities it faces, business leaders cant make fully informed decisions. IoT devices can provide the data businesses need to get a better understanding of these risks. Machine learning can take it a step further and find points of concern in this data that humans could miss.

IoT devices can gather data about the workplace or customers that machine learning programs then process. For example, Progressive has made more than 1.7 trillion observations about its customers driving habits through Snapshot, an IoT tracking device. These analytics help the company adjust clients insurance rates based on the dangers their driving presents.

Business risks arent the only hazards the Internet of Things and machine learning can predict. IoT air quality sensors could alert businesses when to change HVAC filters to protect employee health. Similarly, machine learning cybersecurity programs could sense when hackers are trying to infiltrate a companys network.

Another way the IoT and machine learning could transform your business is by eliminating waste. Data from IoT sensors can reveal where the company could be using more resources than it needs. Machine learning algorithms can then analyze this data to suggest ways to improve.

One of the most common culprits of waste in businesses is energy. Thanks to various inefficiencies, 68% of power in America ends up wasted. IoT sensors can measure where this waste is happening, and with machine learning, adjust to stop it.

Machine learning algorithms in conjunction with IoT devices could restrict energy use, so processes only use what they need. Alternatively, they could suggest new workflows or procedures that would be less wasteful. While many of these steps may seem small, they add up to substantial savings.

Without the IoT and machine learning, businesses cant reach their full potential. These technologies enable savings companies couldnt achieve otherwise. As they advance, theyll only become more effective.

The Internet of Things and machine learning are reshaping the business world. Those that dont take advantage of them now could soon fall behind.

Emily Newton is the Editor-in-Chief of Revolutionized, a magazine exploring how innovations change our world. She has over 3 years experience writing articles in the industrial and tech sectors.

See the article here:
5 Ways the IoT and Machine Learning Improve Operations - BOSS Magazine

Longmont, CO, Feb. 09, 2021 (GLOBE NEWSWIRE) -- Parascript, which provides document analysis software processing for over 100 billion documents each year, announced today the Smart-Force (SFORCE) and Parascript partnership to provide a digital workforce that augments operations by combining cognitive Robotic Process Automation (RPA) technology with customers current investments for high scalability, improved accuracy and an enhanced customer experience in Mexico and across Latin America.

Partnering with Smart-Force means we get to help solve some of the greatest digital transformation challenges in Intelligent Document Processing instead of just the low-hanging fruit. Smart-Force is forward-thinking and committed to futureproofing their customers processes, even with hard-to-automate, unstructured documents where the application of techniques such as NLP is often required, said Greg Council, Vice President of Marketing and Product Management at Parascript. Smart-Force leverages bots to genuinely collaborate with staff so that the staff no longer have to spend all their time on finding information, and performing data entry and verification, even for the most complex multi-page documents that you see in lending and insurance.

Smart-Force specializes in digital transformation by identifying processes in need of automation and implementing RPA to improve those processes so that they run faster without errors. SFORCE routinely enables increased productivity, improves customer satisfaction, and improves staff morale through leveraging the technology of Automation Anywhere, Inc., a leader in RPA, and now Parascript Intelligent Document Processing.

As intelligent automation technology becomes more ubiquitous, it has created opportunities for organizations to ignite their staff towards new ways of working freeing up time from the manual tasks to focus on creative, strategic projects, what humans are meant to do, said Griffin Pickard, Director of Technology Alliance Program at Automation Anywhere. By creating an alliance with Parascript and Smart-Force, we have enabled customers to advance their automation strategy by leveraging ML and accelerate end-to-end business processes.

Our focus at SFORCE is on RPA with Machine Learning to transform how customers are doing things. We dont replace; we compliment the technology investments of our customers to improve how they are working, said Alejandro Castrejn, Founder of SFORCE. We make processes faster, more efficient and augment their staff capabilities. In terms of RPA processes that focus on complex document-based information, we havent seen anything approach what Parascript can do.

We found that Parascript does a lot more than other IDP providers. Our customers need a point-to-point RPA solution. Where Parascript software becomes essential is in extracting and verifying data from complex documents such as legal contracts. Manual data entry and review produces a lot of errors and takes time, said Barbara Mair, Partner at SFORCE. Using Parascript software, we can significantly accelerate contract execution, customer onboarding and many other processes without introducing errors.

The ability to process simple to very complex documents such as unstructured contracts and policies within RPA leveraging FormXtra.AI represents real opportunities for digital transformation across the enterprise. FormXtra.AI and its Smart Learning allow for easy configuration, and by training the systems on client-specific data, the automation is rapidly deployed with the ability to adapt to new information introduced in dynamic production environments.

About SFORCE, S.A. de C.V.

SFORCE offers services that allow customers to adopt digital transformation at whatever pace the organization needs. SFORCE is dedicated to helping customers get the most out of their existing investments in technology. SFORCE provides point-to-point solutions that combine existing technologies with next generation technology, which allows customers to transform operations, dramatically increase efficiency as well as automate manual tasks that are rote and error-prone, so that staff can focus on high-value activities that significantly increase revenue. From exploring process automation to planning a disruptive change that ensures high levels of automation, our team of specialists helps design and implement the automation of processes for digital transformation. Visit SFORCE.

About Parascript

Parascript software, driven by data science and powered by machine learning, configures and optimizes itself to automate simple and complex document-oriented tasks such as document classification, document separation and data entry for payments, lending and AP/AR processes. Every year, over 100 billion documents involved in banking, insurance, and government are processed by Parascript software. Parascript offers its technology both as software products and as software-enabled services to our partners. Visit Parascript.

Read more from the original source:
Parascript and SFORCE Partner to Leverage Machine Learning Eliminating Barriers to Automation - GlobeNewswire

A recommendation engine is a class of machine learning algorithm that suggests products, services, information to users based on analysis of data. Robust recommendation systems are the key differentiator in the operations of big companies like Netflix, Amazon, and Byte Dance (TikTok parent) etc.

Alok Menthe, Data Scientist at Ericsson, gave an informative talk on building Custom recommendation engines for real-world problems at the Machine Learning Developers Summit (MLDS) 2021. Whenever a niche business problem comes in, it has complicated intertwined ways of working. Standard ML techniques may be inadequate and might not serve the customers purpose. That is where the need for a custom-made engine comes in. We were also faced with such a problem with our service network unit at Ericsson, he said.

Menthe said the unit wanted to implement a recommendation system to provide suggestions for assignment workflow a model to delegate the incoming projects to the most appropriate team or resource pool

Credit: Alok Menthe

There were three kinds of data available:

Pool definition data: It relates to the composition of a particular resource poolthe number of people, their competence, and other metadata.

Historical demand data: This kind of data helps in establishing a relationship between the feature demand and a particular resource pool.

Transactional data: It is used for operational purposes.

Menthe said building a custom recommendation system in this context involves the following steps:

Credit: Alok Menthe

After building our model, the most difficult part was feature engineering, which is imperative for building an efficient system. Among the two major modules classification and clusteringwe faced challenges with respect to the latter. We had only categorical information making it difficult to find distances within the objects. We went out of the box to see if we can do any special encoding for the data. We adopted data encoding techniques and frequency-based encoding in this regard, said Menthe.

Clustering module: For this module, initially the team implemented K-modes and agglomerative. However, the results were far from perfect, prompting the team to consider the good-old K-means algorithm. For evaluation purposes, it was done manually with the help of subject matter experts.

The final model had 700 resource pools condensed to 15 pool clusters.

Classification module: For this module, three kinds of algorithm iterations were usedRandom Forest, Artificial Neural Network, XGBoost. Classification accuracy was used as an evaluation metric. Finally, upon 50,00,000 training records, this module demonstrated an accuracy of 71 percent.

Menthe said this recommendation model is monitored on a fortnightly basis by validating the suggested pools against the allocated pools for project demands:

The model has proved to be successful on three fronts:

Menthe summarised the three major takeaways from this project in his concluding remarks: the need to preserve business nuances in ML solutions; thinking beyond standard ML approaches; and understanding that there is no silver bullet ML solution.

I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.

Here is the original post:
There Is No Silver Bullet Machine Learning Solution - Analytics India Magazine