Aims

Prediction models for outcomes in atrial fibrillation (AF) are used to guide treatment. While regression models have been the analytic standard for prediction modelling, machine learning (ML) has been promoted as a potentially superior methodology. We compared the performance of ML and regression models in predicting outcomes in AF patients.

The Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF) and Global Anticoagulant Registry in the FIELD (GARFIELD-AF) are population-based registries that include 74 792 AF patients. Models were generated from potential predictors using stepwise logistic regression (STEP), random forests (RF), gradient boosting (GB), and two neural networks (NNs). Discriminatory power was highest for death [STEP area under the curve (AUC) = 0.80 in ORBIT-AF, 0.75 in GARFIELD-AF] and lowest for stroke in all models (STEP AUC = 0.67 in ORBIT-AF, 0.66 in GARFIELD-AF). The discriminatory power of the ML models was similar or lower than the STEP models for most outcomes. The GB model had a higher AUC than STEP for death in GARFIELD-AF (0.76 vs. 0.75), but only nominally, and both performed similarly in ORBIT-AF. The multilayer NN had the lowest discriminatory power for all outcomes. The calibration of the STEP modelswere more aligned with the observed events for all outcomes. In the cross-registry models, the discriminatory power of the ML models was similar or lower than the STEP for most cases.

When developed from two large, community-based AF registries, ML techniques did not improve prediction modelling of death, major bleeding, or stroke.

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Machine Learning Does Not Improve Upon Traditional Regression in Predicting Outcomes in Atrial Fibrillation: An Analysis of the ORBIT-AF and...

Global Machine Learning in Medical Imaging Industry: with growing significant CAGR during Forecast 2020-2025

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The market research report on the global Machine Learning in Medical Imaging industry provides a comprehensive study of the various techniques and materials used in the production of Machine Learning in Medical Imaging market products. Starting from industry chain analysis to cost structure analysis, the report analyzes multiple aspects, including the production and end-use segments of the Machine Learning in Medical Imaging market products. The latest trends in the pharmaceutical industry have been detailed in the report to measure their impact on the production of Machine Learning in Medical Imaging market products.

Leading key players in the Machine Learning in Medical Imaging market are Zebra, Arterys, Aidoc, MaxQ AI, Google, Tencent, Alibaba

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Product Types:, Supervised Learning, Unsupervised Learning, Semi Supervised Learning, Reinforced Leaning

By Application/ End-user:, Breast, Lung, Neurology, Cardiovascular, Liver

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North America(the United States, Canada, and Mexico)Europe(Germany, France, UK, Russia, and Italy)Asia-Pacific(China, Japan, Korea, India, and Southeast Asia)South America(Brazil, Argentina, Colombia, etc.)The Middle East and Africa(Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

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Whats in the offering: The report provides in-depth knowledge about the utilization and adoption of Machine Learning in Medical Imaging Industries in various applications, types, and regions/countries. Furthermore, the key stakeholders can ascertain the major trends, investments, drivers, vertical players initiatives, government pursuits towards the product acceptance in the upcoming years, and insights of commercial products present in the market.

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Lastly, the Machine Learning in Medical Imaging Market study provides essential information about the major challenges that are going to influence market growth. The report additionally provides overall details about the business opportunities to key stakeholders to expand their business and capture revenues in the precise verticals. The report will help the existing or upcoming companies in this market to examine the various aspects of this domain before investing or expanding their business in the Machine Learning in Medical Imaging market.

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Machine Learning in Medical Imaging Market 2020 : Analysis by Geographical Regions, Type and Application Till 2025 | Zebra, Arterys, Aidoc, MaxQ AI -...

Machine learning algorithms will learn patterns from the past data and predict trends and best price. These algorithms can predict the best price, discount price and promotional price based on competition, macroeconomic variables, seasonality etc.

To find out the correct pricing in real-time retailers follow the following steps:

Gather input data

In order to build a machine learning algorithm, retailers collect various data points from the customers. These are:

Transactional data

This includes the sales history of each customer and the products, which they have bought in the past.

Product description

The brands, product category, style, photos and the selling price of the previously sold products are collected. Past promotions and campaigns are also analysed to find the effect of price changes on each category.

Customer details

Demographic details and customer feedback are gathered.

Competition and inventory

Retailers also try to find the data regarding the price of products sold by their competitors and supply chain and inventory data.

Depending on the set of key performance indicators defined by the retailers, the relevant data is filtered.

For every industry, pricing would involve different goals and constraints. In terms of the dynamic nature, the retail industry can be compared to the casino industry where machine learning is involved inonline live dealer casino games too.

Like casinos, retail also has the target of profit maximisation and retention of customer loyalty. Each of these goals and constraints can be fed to a machine learning algorithm to generate dynamic prices of products.

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news and analysis for omnichannel retailers - Retail Technology Innovation Hub

This is a problem if we want AIs to be trustworthy. Thats why Diffbot takes a different approach. It is building an AI that reads every page on the entire public web, in multiple languages, and extracts as many facts from those pages as it can.

Like GPT-3, Diffbots system learns by vacuuming up vast amounts of human-written text found online. But instead of using that data to train a language model, Diffbot turns what it reads into a series of three-part factoids that relate one thing to another: subject, verb, object.

Pointed at my bio, for example, Diffbot learns that Will Douglas Heaven is a journalist; Will Douglas Heaven works at MIT Technology Review; MIT Technology Review is a media company; and so on. Each of these factoids gets joined up with billions of others in a sprawling, interconnected network of facts. This is known as a knowledge graph.

Knowledge graphs are not new. They have been around for decades, and were a fundamental concept in early AI research. But constructing and maintaining knowledge graphs has typically been done by hand, which is hard. This also stopped Tim Berners-Lee from realizing what he called the semantic web, which would have included information for machines as well as humans, so that bots could book our flights, do our shopping, or give smarter answers to questions than search engines.

A few years ago, Google started using knowledge graphs too. Search for Katy Perry and you will get a box next to the main search results telling you that Katy Perry is an American singer-songwriter with music available on YouTube, Spotify, and Deezer. You can see at a glance that she is married to Orlando Bloom, shes 35 and worth $125 million, and so on. Instead of giving you a list of links to pages about Katy Perry, Google gives you a set of facts about her drawn from its knowledge graph.

But Google only does this for its most popular search terms. Diffbot wants to do it for everything. By fully automating the construction process, Diffbot has been able to build what may be the largest knowledge graph ever.

Alongside Google and Microsoft, it is one of only three US companies that crawl the entire public web. It definitely makes sense to crawl the web, says Victoria Lin, a research scientist at Salesforce who works on natural-language processing and knowledge representation. A lot of human effort can otherwise go into making a large knowledge base. Heiko Paulheim at the University of Mannheim in Germany agrees: Automation is the only way to build large-scale knowledge graphs.

To collect its facts, Diffbots AI reads the web as a human wouldbut much faster. Using a super-charged version of the Chrome browser, the AI views the raw pixels of a web page and uses image-recognition algorithms to categorize the page as one of 20 different types, including video, image, article, event, and discussion thread. It then identifies key elements on the page, such as headline, author, product description, or price, and uses NLP to extract facts from any text.

Every three-part factoid gets added to the knowledge graph. Diffbot extracts facts from pages written in any language, which means that it can answer queries about Katy Perry, say, using facts taken from articles in Chinese or Arabic even if they do not contain the term Katy Perry.

Browsing the web like a human lets the AI see the same facts that we see. It also means it has had to learn to navigate the web like us. The AI must scroll down, switch between tabs, and click away pop-ups. The AI has to play the web like a video game just to experience the pages, says Tung.

Diffbot crawls the web nonstop and rebuilds its knowledge graph every four to five days. According to Tung, the AI adds 100 million to 150 million entities each month as new people pop up online, companies are created, and products are launched. It uses more machine-learning algorithms to fuse new facts with old, creating new connections or overwriting out-of-date ones. Diffbot has to add new hardware to its data center as the knowledge graph grows.

Researchers can access Diffbots knowledge graph for free. But Diffbot also has around 400 paying customers. The search engine DuckDuckGo uses it to generate its own Google-like boxes. Snapchat uses it to extract highlights from news pages. The popular wedding-planner app Zola uses it to help people make wedding lists, pulling in images and prices. NASDAQ, which provides information about the stock market, uses it for financial research.

Adidas and Nike even use it to search the web for counterfeit shoes. A search engine will return a long list of sites that mention Nike trainers. But Diffbot lets these companies look for sites that are actually selling their shoes, rather just talking about them.

For now, these companies must interact with Diffbot using code. But Tung plans to add a natural-language interface. Ultimately, he wants to build what he calls a universal factoid question answering system: an AI that could answer almost anything you asked it, with sources to back up its response.

Tung and Lin agree that this kind of AI cannot be built with language models alone. But better yet would be to combine the technologies, using a language model like GPT-3 to craft a human-like front end for a know-it-all bot.

Still, even an AI that has its facts straight is not necessarily smart. Were not trying to define what intelligence is, or anything like that, says Tung. Were just trying to build something useful.

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This know-it-all AI learns by reading the entire web nonstop - MIT Technology Review

Python was released in the 1990s as a general-purpose programming language. Despite its clean syntax, the exposure Python got in its first decade wasntencouraging, and itdidnt really find inroads into the developers workspace. Perl was the first choice scripting language and Java had established itself as the go-to in the object-oriented programming arena. Of course, any language takes time to mature and only gets adopted when its better suited to a task than the existing tools.

For Python, that time first arrived during the early 2000s when people started realizing it has an easier learning curve than Perl and offers interoperability with other languages. This realization led to a larger number of developers incorporating Python into their applications. The emergence of Django eventually led to the doom of Perl, and Python started gaining more momentum. Still, it wasnt even close in popularity to Java and JavaScript, both of which were newer than Python.

Fast forward to the present, and Python has trumped Java to become the second-most-popular language according to the StackOverflow Developer Survey 2019. It was also the fastest-growing programming language of the previous decade. Pythons rise in popularity has a lot to do with the emergence of big data in the 2010s as well asdevelopments in machine learning and artificial intelligence. Businesses urgently required a language for quick development with low barriers of entry that could help manage large-scale data and scientific computing tasks. Python was well-suited to all these challenges.

Besides having those factors in its favor, Python was an interpreted language with dynamic typing support. More importantly, it had the backing of Google, whod invested in Python for Tensorflow, which led to its emergence as the preferred language for data analysis, visualization, and machine learning.

Yet, despite the growing demand for machine learning and AI at the turn of this decade, Python wont stay around for long. Like every programming language, it has its own set of weaknesses. Those weaknesses make it vulnerable to replacement by languages more suited to the common tasks businesses ask of them. Despite the presence of R, the emergence of newer languages such as Swift, Julia, and Rust actually poses a bigger threat to the current king of data science.

Rust is still trying to catch up with the machine learning community, and so I believe Swift and Julia are the languages that will dethrone Python and eventually rule data science. Lets see why odds are against Python.

All good things come at a cost, and Pythons dynamically typed nature is no exception. It hampers developers, especially when running the code in production. Dynamic typing that makes it easy to write code quickly without defining types increases the risk of running into runtime issues, especially when the codebase size increases. Bugs that a compiler would easily figure out could go unidentified in Python, causing hindrances in production and ultimately slowing the development process in large-scale applications.

Worse, unlike compiled code, Pythons interpreter analyzes every line of code at execution time. This leads to an overhead that causes a significantly slower performance when compared to other languages.

Julia allows you to avoid some of these problems. Despite being dynamically typed, it has a just-in-time compiler. The JIT compiler either generates the machine code right before its executed or uses previously stored, cached compilations, which makes it as performant as statically typed languages. More importantly, it has a key feature known as multiple dispatch thatis like function overloading of OOPs, albeit at runtime. The power of multiple dispatch lies in its ability to handle different argument types without the need to create separate function names or nested if statements. This helps in writing compact code, which is a big win in numeric computations since unlike Python, you can easily scale solutions to deal with all types of arguments.

Even better, Swift is a statically typed language and is highly optimized due to its LLVM (Low-Level Virtual Machine) compiler. The LLVM makes it possible to quickly compile into assembly code, making Swift super-efficient and almost as fast as C. Also, Swift boasts better memory safety and management tools known as Automatic Reference Counting. Unlike garbage collectors, ARC is a lot more deterministic as it reclaims memory whenever the reference count hits zero.

As compiled languages that offer type annotations, Swift and Julia are a lot faster and robust for development than Python. That alone might be enough to recommend them over the older language, but there are other factors to consider as well.

If slowness was not the most obvious drawback of Python, the language also has limitations with parallel computing.

In short, Python uses GIL (Global Interpreter Lock), which prevents multiple threads from executing at the same time in order to boost the performance of single threads. This process is a big hindrance because it means that developers cannot use multiple CPU cores for intensive computing.

I agree withthe commonplace notion that were currently doing finewhen leveraging Pythons interoperability with C/C++ libraries like Tensorflow and PyTorch. But a Python wrapper doesnt solve all debugging issues. Ultimately, when inspecting the underlying low-level code, were falling back on C and C++. Essentially, we cant leverage the strengths of Python at the low level,which puts it out of the picture.

This factor will soon play a decisive role in the fall of Python and rise of Julia and Swift. Julia is a language exclusively designed to address the shortcomings of Python. It primarily offers three features: coroutines (asynchronous tasks), multi-threading, and distributed computing all of which only show the immense possibilities for concurrent and parallel programming. This structure makes Julia capable of performing scientific computations and solving big data problems at a far greater speed than Python.

On the other hand, Swift possesses all the tools required for developing mobile apps and has no problems with parallel computing.

Despite the disadvantages it has with respect to speed, multi-threading, and type-safety, Python still has a huge ecosystem that boasts an enormous set of libraries and packages. Understandably, Swift and Julia are still infants in the field of machine learning and possess only a limited number of libraries. Yet, their interoperability with Python more than compensates for the lack of library support in Julia and Swift.

Julia not only lets programmers use Python code (and vice-versa), but also supports interoperability with C, R, Java, and almost every major programming language. This versatility would certainly give the language a good boost and increase its chances of a quick adoption among data scientists.

Swift, on the other hand, provides interoperability with Python with the PythonKit library.The biggest selling point for Swift (which has an Apple origin) is a strong support its been getting from Google, who fully backed Python decades ago. See how the tables have turned!

Also, the fact that the creator of Swift, Chris Lattner, is now working on Googles AI brain team just shows that Swift is being seriously groomed as Pythons replacement in the machine learning field.The Tensorflow team investing in Swift with their S4TF project only further proves that the language isnt merely regarded as a wrapper over Python. Instead Swift, thanks to its differential programming support and ability to work at a low level like C, will potentially be used to replace the underlying deep learning tools.

As the size of data continues to increase, Pythons Achilles heel will be soon found out. Gone are the days when ease of use and ability to write code quickly mattered. Speed and parallel computing are the name of the game and Python, which is a more general-purpose language, will no longer solve that problem. Inevitably, it will fade away while Julia and Swift seem like the candidates to take over the reins.

Its important to note that Python as a programming language wont disappear any time soon. Instead, itll only take a backseat in data science as the languages that are more specifically designed for deep learning will rule.

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Python Is About to Get the Squeeze - Built In