{"id":4219,"date":"2020-12-15T03:14:21","date_gmt":"2020-12-15T03:14:21","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/12\/15\/predictive-analytics-ml-ai-do-i-have-to-choose\/"},"modified":"2020-12-15T03:14:21","modified_gmt":"2020-12-15T03:14:21","slug":"predictive-analytics-ml-ai-do-i-have-to-choose","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/12\/15\/predictive-analytics-ml-ai-do-i-have-to-choose\/","title":{"rendered":"Predictive Analytics, ML, AI \u2013 Do I Have to Choose?"},"content":{"rendered":"

Predictive Analytics, ML, AI \u2013 Do I Have to Choose?<\/a><\/p>\n

<\/p>\n

<\/p>\n

As you understand what AI, ML, predictive and prescriptive analytics are, you will see what analytics you need in your organization and how each type can help you.<\/p>\n

<\/strong><\/p>\n

There is plenty of confusion about which is better, predictive analytics, machine learning (ML), or artificial intelligence (AI). Which one should a company choose to use? The short answer is, you shouldn\u2019t choose. Each of these aspects of data analysis may be useful to your company, and they have strong relationships with each other. The more detailed answer requires a deeper understanding of what these terms mean and how they relate to each other. <\/p>\n

Defining the Options: Predictive analytics, ML, and AI<\/strong><\/h3>\n

Predictive analytics<\/strong> is analyzing past events to forecast what might happen next. By understanding the past trends thoroughly and analyzing what factors contributed to those trends, we can predict how current factors will affect the future with some level of accuracy. While its never certain \u2013 predictive analytics is far more often correct about future predictions than simply relying on human experience or hunches. Predictive analytics provides probable answers to questions like Will demand for my product be higher next month? Will a part in this machine fail in the next year? Will next quarter\u2019s profits be lower than last year? <\/p>\n

Predictive analytics is usually contrasted with descriptive and prescriptive analytics. Descriptive analytics<\/strong> describes what is currently happening or what has happened. Whilst prescriptive analytics<\/a><\/strong> involves analyzing past events to recommend what decision should be made next. <\/p>\n

Naturally, trying to predict the future is considerably
\nharder and often requires more data to be reasonably accurate a fair amount of
\nthe time. All of these require careful analysis of data, but in general,
\ndescriptive analytics is the simplest. Prescriptive analytics goes a step
\nfurther, is that much more complex, and often requires even more data and even
\nmore intense forms of analysis. <\/p>\n

Machine learning (ML)<\/strong> is a process that uses
\nmathematical models called algorithms to make sense of data in one way or
\nanother. The math may determine things like which data should be grouped
\ntogether because it is similar, or what patterns exist in data that are not
\neasy for a human to find, or what data factors influence a change in another data
\nset. Machine learning gets its name because a single mathematical algorithm may
\nbe used for a wide variety of purposes. It is the data that determines the
\noutcome, not a programmer who writes code instructions. Different data will indicate
\nto the algorithm different things, teaching the ML model about itself and data
\nlike it. Hence, the machine learns from the data.<\/p>\n

The more data that can be provided to train an ML algorithm,
\nthe more accurate the ML model will become, the more it will learn about that
\ndata. It often requires a LOT of data to make a good ML model, and it often
\ntakes a powerful computational engine to train the model with all of that data.<\/p>\n

Once trained, an ML model can understand a dataset to the
\npoint where it may be used to predict where it will go from here (predictive
\nanalytics), or it may find patterns in data that indicate what action would
\naffect the data in a desirable way (prescriptive analytics). So, machine learning
\nis a sophisticated method of data analysis that can be used for both predictive
\nand prescriptive analytics.<\/p>\n

Artificial Intelligence (AI)<\/strong> is a general term that indicates any form of technology where a machine mimics the capabilities of a person. An example is natural language processing<\/a> (NLP), where you speak to a machine in your own natural language, and the machine translates that into some form of instruction. It may execute that instruction, perhaps finding information for you, and then it may provide the answer back to you also in your own language (Natural Language Generation (NLG)).<\/p>\n

Artificial intelligence also includes robotics and
\nautomation, where a machine recognizes a certain condition, either via coded
\ninstructions or from a pattern it recognizes due to ML, and does something
\nsimilar to something a human would do under those conditions. <\/p>\n

Making a Decision<\/strong><\/h3>\n

So, back to the original question: Which is better, AI, ML,
\nor predictive analytics, and what do I choose? You might as well ask which is
\nbetter \u2013 a seed, an apple, or a tree. They are all interrelated, and which you
\nuse depends on your need. If I needed to grow something next spring, a seed
\nwould work best. If you were hungry now, an apple would be the clear choice. If
\nyou wanted shade, the tree would definitely be the way to go. But if you plant
\nthe seed, it will grow into a tree that bears apples, so they are all part of
\neach other. <\/p>\n

Based on your business requirement, you will choose what you
\nneed. <\/p>\n

For example, if your business is manufacturing, you might
\nuse ML to analyze the sensor data from the components of the manufacturing
\nmachines to determine if theres a pattern that predicts (predictive analytics)
\nwhen a component will break soon. Then, you might have that machine
\nautomatically stop before it breaks, and send a signal recommending
\n(prescriptive analytics) that youll replace a certain part. This preventative
\nmaintenance system would be a good example of artificial intelligence in
\npractical use.<\/p>\n

Another example might be if you were an online retailer. You
\nmight use descriptive analytics to determine that there are a large number of
\nblue sweaters in your inventory. Then you might use machine learning to find
\nthe group of people who buy from you who tend to like blue clothing and live in
\nan area that will get chilly soon, so they are likely to need a sweater in the
\nnear future (predictive analytics). You might then automatically recommend
\n(prescriptive analytics) a blue sweater to them the next time they are online,
\nespecially if they purchase blue pants or maybe gloves. This is another good
\nexample of artificial intelligence used in business.<\/p>\n

Accuracy in Time to Act<\/strong><\/h3>\n

As you come to understand that AI, ML, predictive and
\nprescriptive analytics arent just buzzwords invented to sound complex, you
\nwill often begin to see what analytics you need in your organization and how
\neach type can help you. When you get ready to put advanced analytics into
\npractice, remember these three things that will help you make it work. <\/p>\n

    \n
  1. Accuracy Using all available data vastly
    \nimproves the accuracy of ML models. Dont settle for taking a sample. Make sure
    \nthat your technology and methods allow full analysis of all relevant available
    \ndata. Also, over time ML models become less and less accurate as data and
    \nconditions change. Be sure to manage, measure accuracy, retrain, and update
    \nmodels as often as necessary to maintain the highest possible accuracy.<\/li>\n
  2. Action The best information, prediction, or
    \nrecommendation in the world will be worthless if you do not take appropriate
    \naction in the moment when it will do the most good. <\/li>\n
  3. Automation Allow the machine to do what it
    \ndoes best. Automated monitoring, analytics, and sometimes even automatically
    \ntriggered actions can take best advantage of rapidly changing business
    \nsituations. <\/li>\n<\/ol>\n

    The more advanced forms of analytics can provide companies
    \nwith a huge competitive edge over their competitors who dont use them yet.
    \nUnderstanding the terms and what they mean in the context of your business is
    \nthe first step to reaping the benefits.<\/p>\n<\/p>\n

    Published at Tue, 15 Dec 2020 01:18:45 +0000<\/p>\n

    Top 10 AI and machine learning stories of 2020<\/a><\/p>\n

    <\/div>\n

    Toward the tail end of pre-pandemic 2019, Mayo Clinic Chief Information Officer Cris Ross stood on a stage in California and declared, “This artificial intelligence stuff is real<\/a>.”<\/p>\n

    Indeed, while some may argue that AI and machine learning might have been harnessed better<\/a> during the early days of COVID-19, and while the risk of algorithmic bias<\/a> is very real, there’s little question that artificial intelligence, evolving and maturing by the day for an array of use cases across healthcare.<\/p>\n

    Here are the most-read stories about AI during this most unusual year.<\/p>\n

    UK to use AI for COVID-19 vaccine side effects<\/strong><\/a>. On a day when vaccines, developed in record time, first begin to be administered in the U.S., it’s worth remembering AI’s crucial role in helping the world get to this hopefully pivotal moment.<\/p>\n

    AI algorithm IDs abnormal chest X-rays from COVID-19 patients<\/a>. <\/strong>Machine learning has been a hugely valuable diagnostic tool as well, as illustrated by this story about a tool from cognitive computing vendor behold.ai that promises ‘instant triage” based on lung scans \u2013 offering faster diagnosis of COVID-19 patients and helping with resource allocation.<\/p>\n

    How AI use cases are evolving in the time of COVID-19<\/a><\/strong>. In a HIMSS20 Digital presentation, leaders from Google Cloud, Nuance and Health Data Analytics Institute shared perspective on how AI and automation were being deployed for pandemic response \u2013 from the hunt for therapeutics and vaccines to analytics to optimize revenue cycle strategies. <\/p>\n

    Microsoft launches major $40M AI for Health initiative<\/a><\/strong>. The company said the the five-year AI for Health (part of its $165 million AI for Good initiative) will help healthcare organizations around the world deploy with leading edge technologies in the service of three key areas: accelerating medical research, improving worldwide understanding to protect against global health crises such as COVID-19 and reducing health inequity.<\/p>\n

    How AI and machine learning are transforming clinical decision support<\/a><\/strong>. “Today\u2019s digital tools only scratch the surface,” said Mayo Clinic Platform President Dr. John Halamka. “Incorporating newly developed algorithms that take advantage of machine learning, neural networks, and a variety of other types of artificial intelligence can help address many of the shortcomings of human intelligence.” <\/p>\n

    Clinical AI vendor Jvion unveils COVID Community Vulnerability Map<\/a><\/strong>. In the very early days of the pandemic, clinical AI company Jvion launched this intereactive map, which tracks the social determinants of health, helping identify populations down to the census-block level that are at risk for severe outcomes. <\/p>\n

    AI bias may worsen COVID-19 health disparities for people of color<\/a><\/strong>. An article in the Journal of the American Medical Informatics Association asserts that biased data models could further the disproportionate impact the COVID-19 pandemic is already having on people of color. “If not properly addressed, propagating these biases under the mantle of AI has the potential to exaggerate the health disparities faced by minority populations already bearing the highest disease burden,” said researchers.<\/p>\n

    The origins of AI in healthcare, and where it can help the industry now<\/a><\/strong>. “The intersection of medicine and AI is really not a new concept,” said Dr. Taha Kass-Hout, director of machine learning and chief medical officer at Amazon Web Services. (There were limited chatbots and other clinical applications as far back as the mid-60s.) But over the past few years, it has become ubiquitous across the healthcare ecosystem. “Today, if you\u2019re looking at PubMed, it cites over 12,000 publications with deep learning, over 50,000 machine learning,” he said.<\/p>\n

    AI, telehealth could help address hospital workforce challenges<\/a><\/strong>. “Labor is the largest single cost for most hospitals, and the workforce is essential to the critical mission of providing life-saving care,” noted a January American Hospital Association report on the administrative, financial, operational and clinical uses of artificial intelligence. “Although there are challenges, there also are opportunities to improve care, motivate and re-skill staff, and modernize processes and business models that reflect the shift toward providing the right care, at the right time, in the right setting.”<\/p>\n

    AI is helping reinvent CDS, unlock COVID-19 insights at Mayo Clinic<\/a><\/strong>. In a HIMSS20 presentation, JohnHalamka shared some of the most promising recent clinical decision support advances at the Minnesota health system \u2013 and described how they’re informing treatment decisions for an array of different specialties and helping shape its understanding of COVID-19. “Imagine the power [of] an AI algorithm if you could make available every pathology slide that has ever been created in the history of the Mayo Clinic,” he said. “That’s something we’re certainly working on.”<\/p>\n

    Twitter: @MikeMiliardHITN<\/a>
    Email the writer:     mike.miliard@himssmedia.com<\/a><\/em>
    Healthcare IT News is a HIMSS publication.<\/em><\/p>\n<\/p>\n

    Published at Mon, 14 Dec 2020 22:18:45 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

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