Mediaboss Marketing

With healthcare organizations around the world leveraging cloud technologies for key clinical and operational systems, the industry is building toward digitally enhanced, data-driven healthcare.

And unstructured healthcare data, within clinical documents and summaries, continues to remain an important source of insights to support clinical and operational excellence.

But there are countless nuggets of important unstructured data something that does not lend itself to manual search and manipulation by clinicians. This is where automation comes in.

Arun Ravi, senior product leader at Amazon Web Services is copresenting a HIMSS20 Digital presentation on unstructured healthcare data and machine learning, Accelerating Insights from Unstructured Data, Cloud Capabilities to Support Healthcare.

There is a huge shift from volume- to value-based care: 54% of hospital CEOs see the transition from volume to value as their biggest financial challenge, and two-thirds of the IT budget goes toward keeping the lights on, Ravi explained.

Machine learning has this really interesting role to play where were not necessarily looking to replace the workflows, but give essentially a superpower to people in healthcare and allow them to do their jobs a lot more efficiently.

In terms of how this affects health IT leaders, with value-based care there is a lot of data being created. When a patient goes through the various stages of care, there is a lot of documentationa lot of datacreated.

But how do you apply the resources that are available to make it much more streamlined, to create that perfect longitudinal view of the patient? Ravi asked. A lot of the current IT models lack that agility to keep pace with technology. And again, its about giving the people in this space a superpower to help them bring the right data forward and use that in order to make really good clinical decisions.

This requires responding to a very new model that has come into play. And this model requires focus on differentiating a healthcare organizations ability to do this work in real time and do it at scale.

How [do] you incorporate these new technologies into care delivery in a way that not only is scalable but actually reaches your patients and also makes sure your internal stakeholders are happy with it? Ravi asked. And again, you want to reduce the risk, but overall, how do you manage this data well in a way that is easy for you to scale and easy for you to deploy into new areas as the care model continues to shift?

So why is machine learning important in healthcare?

If you look at the amount of unstructured data that is created, it is increasing exponentially, said Ravi. And a lot of that remains untapped. There are 1.2 billion unstructured clinical documents that are actually created every year. How do you extract the insights that are valuable for your application without applying manual approaches to it?

Automating all of this really helps a healthcare organization reduce the expense and the time that is spent trying to extract these insights, he said. And this creates a unique opportunity, not just to innovate, but also to build new products, he added.

Ravi and his copresenter, Paul Zhao, senior product leader at AWS, offer an in-depth look into gathering insights from all of this unstructured healthcare data via machine learning and cloud capabilities in their HIMSS20 Digital session. To attend the session, click here.

Twitter:@SiwickiHealthITEmail the writer:bill.siwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Machine learning can give healthcare workers a 'superpower' - Healthcare IT News

TLDR: The Complete 2020 Big Data and Machine Learning Bundle breaks down understanding and getting started in two of the tech eras biggest new growth sectors.

Its instructive to know just how big Big Data really is. And the reality is that its now so big that the word big doesnt even effectively do it justice anymore. Right now, humankind is creating 2.5 quintillion bytes of data every day. And its growing exponentially, with 90 percent of all data created in just the past two years. By 2023, the big data industry will be worth about $77 billion and thats despite the fact that unstructured data is identified as a problem by 95 percent of all businesses.

Meanwhile, data analysis is also the background of other emerging fields, like the explosion of machine learning projects that have companies like Apple scooping up machine learning upstarts.

The bottom is that if you understand Big Data, you can effectively right your own ticket salary-wise. You can jump into this fascinating field the right way with the training in The Complete 2020 Big Data and Machine Learning Bundle, on sale now for $39.90, over 90 percent off from TNW Deals.

This collection includes 10 courses featuring 68 hours of instruction covering the basics of big data, the tools data analysts need to know, how machines are being taught to think for themselves, and the career applications for learning all this cutting-edge technology.

Everything starts with getting a handle on how data scientists corral mountains of raw information. Six of these courses focus on big data training, including close exploration of the essential industry-leading tools that make it possible. If you dont know what Hadoop, Scala or Elasticsearch do or that Spark Streaming is a quickly developing technology for processing mass data sets in real-time, you will after these courses.

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Prices are subject to change.

Read next: The 'average' Robinhood trader is no match for the S&P 500, just like Buffett

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Big data and machine learning are growing at massive rates. This training explains why - The Next Web

At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.

In a digital world full of ever-expanding datasets like these, its not always possible for humans to analyze such vast troves of information themselves. Thats why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.

Our latest video explainer part of our Methods 101 series explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about how weve used machine learning and other computational methods in our research, including the analysis mentioned in this video, you can explore recent reports from our Data Labs team.

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What is machine learning, and how does it work? - Pew Research Center

Last Friday, the USs Dow Jones Index climbed up by almost 1,000 points. The U.S. Labor Department said that the economy unexpectedly added 2.5 million jobs in May. This followed a depressing April, when the country shed as many as 20 million jobs. This lowered the unemployment rate to roughly 13%, versus the 15% it had hit in April. The report also surprised economists and analysts who had forecast millions more losing their jobs. Their Machine Learning (ML) models were predicting that the jobless rate would continue to rise to over 20%.

This isnt the first time that the technology around ML has failed. In 2016, sophisticated ML algorithms failed to predict the outcomes of both the Brexit vote as well as the US presidential election. Some make the argument that algorithm-driven machine prediction was in its infancy in 2016. If thats the case, then what have the intervening four years of computer programming and an explosion of data available to train" deep-learning algorithms really achieved?

As a concept, ML represents the idea that a computer, when fed with enough raw data, can begin on its own to see patterns and rules in these numbers. It can also learn to recognize, categorize and feed new data upon arrival into the patterns and rules already created by the computer program. As more data is received, it adds to the intelligence" of the computer by making its patterns and rules ever more refined and reliable.

There is still a small but pertinent inconvenience that deserves our attention. Despite the great advances in computing, it is still very difficult to teach computers both human context and basic common sense. The brute-force approach of Artificial Intelligence (AI) behemoths does not rely on well-codified rules based on common sense. It relies instead on the raw computing power of machines to sift thousands upon thousands of potential combinations before selecting the best answer using pattern-matching. This applies as much to questions that are intuitively answered by five-year-olds as it does to a medical image diagnosis.

These same algorithms have been guiding decisions made by businesses for a while nowespecially strategic and other shifts in corporate direction based on consumer behaviour. In a world where corporations make binary choices (either path X or path Y, but not both), these algorithms still fall short.

The pandemic has exposed their insufficiency further. This is especially true with ML systems at e-commerce retailers that were initially programmed to make sense of our online behaviour. During the pandemic, our online behaviour has been volatile. News reports in various Western countries that kept e-commerce alive during their lockdowns have focused on retailers trying to optimize toilet paper stocks one week and stay-at-home board games the next.

The disruption in ML is widespread. Our online buying behaviour influences a whole hoard of subsidiary computer systems. These are in areas such as inventory and supply chain management, marketing, pricing, fraud detection and so on.

To an interested observer, it would appear that many of these algorithms base themselves on stationary assumptions about data. A detailed explanation of how stationary processes are used for statistical data modeling and predictions can be found here. Very simply put, this means that algorithms assume that the rules havent changed, or wont change due to some event in the future. Surprisingly, this goes against the basic admonition that almost all professional investors bake into their fine print, especially the one that says, Past performance is no predictor of future performance."

The paradox is that finding patterns and then using them to make useful predictions is what ML is all about in the first place. But static assumptions have meant that the data sets used to train ML models havent included anything more than elementary worst case" information. They didnt expect a pandemic.

Also, bias, even when it is not informed by such negative qualities as racism, is often added into these algorithms long before they spit out computer code. The bias enters through the manner in which an ML solution is framed, the presence of unknown unknowns" in data sets, and in how the data is prepared before it is fed into a computer.

Compounding such biases is the phenomenon of an echo chamber" that is created by finely-targeted algorithms that these companies use. The original algorithms induced users to stay online longer and bombarded them with an echo-chamber overload of information that served to reinforce what the algorithm thinks the searcher needs to know. For instance, if I search for a particular type of phone on an e-commerce site, future searches are likely to auto-complete with that phone showing up even before I key in my entire search string. The algorithm gets thrown off when I search for toilet paper instead.

The situation brought about by the covid pandemic is still volatile and fluid. The training data sets and the computer code they produce to adjust predictive ML algorithms are unequal to the volatility. They need constant manual supervision and tweaking so that they do not throw themselves and other sophisticated downstream automated processes out of gear. It appears that consistent human involvement in automated systems will be around for quite some time.

Siddharth Pai is founder of Siana Capital, a venture fund management company focused on deep science and tech in India

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Opinion | Covid has exposed the limitations of machine learning - Livemint

By Elana Krasner

Companies across industries are exploring and implementing artificial intelligence (AI) projects, from big data to robotics, to automate business processes, improve customer experience, and innovate product development. According to McKinsey, embracing AI promises considerable benefits for businesses and economies through its contributions to productivity and growth. But with that promise comes challenges.

Computers and machines dont come into this world with inherent knowledge or an understanding of how things work. Like humans, they need to be taught that a red light means stop and green means go. So, how do these machines actually gain the intelligence they need to carry out tasks like driving a car or diagnosing a disease?

There are multiple ways to achieve AI, and existential to them all is data. Without quality data, artificial intelligence is a pipedream. There are two ways data can be manipulatedeither through rules or machine learningto achieve AI, and some best practices to help you choose between the two methods.

Long before AI and machine learning (ML) became mainstream terms outside of the high-tech field, developers were encoding human knowledge into computer systems as rules that get stored in a knowledge base. These rules define all aspects of a task, typically in the form of If statements (if A, then do B, else if X, then do Y).

While the number of rules that have to be written depends on the number of actions you want a system to handle (for example, 20 actions means manually writing and coding at least 20 rules), rules-based systems are generally lower effort, more cost-effective and less risky since these rules wont change or update on their own. However, rules can limit AI capabilities with rigid intelligence that can only do what theyve been written to do.

While a rules-based system could be considered as having fixed intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. There is still a layer of underlying rules, but instead of a human writing a fixed set, the machine has the ability to learn new rules on its own, and discard ones that arent working anymore.

In practice, there are several ways a machine can learn, but supervised trainingwhen the machine is given data to train onis generally the first step in a machine learning program. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.

The anticipated benefits to AI are high, so the decisions a company makes early in its execution can be critical to success. Foundational is aligning your technology choices to the underlying business goals that AI was set forth to achieve. What problems are you trying to solve, or challenges are you trying to meet?

The decision to implement a rules-based or machine learning system will have a long-term impact on how a companys AI program evolves and scales. Here are some best practices to consider when evaluating which approach is right for your organization:

When choosing a rules-based approach makes sense:

When to apply machine learning:

The promises of AI are real, but for many organizations, the challenge is where to begin. If you fall into this category, start by determining whether a rules-based or ML method will work best for your organization.

About the author:

Elana Krasner is Product Marketing Manager at 7Park Data, a data and analytics company that transforms raw data into analytics-ready products using machine learning and NLP technologies. She has been in the tech marketing field for almost 10 years and has worked across the industry in Cloud Computing, SaaS and Data Analytics.

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How to choose between rule-based AI and machine learning - TechTalks