Artificial Intelligence Primer: Machine Learning & Deep Learning
04/04/2019 in Blog
Data is, arguably, one of the most important words of the 21st century. As technology and the internet become more and more ingrained in our lives, the amount of data about ourselves, our preferences, our propensities, our lives that we’re knowingly or unknowingly sharing grows exponentially. Sure, this raises concerns over privacy and security and what we really want companies to know about the inner workings of our minds, but if you look at the flipside of things, as marketers we’re also becoming equipped to help transform people’s lives with data, especially in healthcare.
Data has become the tool in the toolbox for marketers, but making sense of the mountains of just ones and zeros to help someone manage their diabetes or take appropriate steps to prevent a heart attack isn’t usually in our skillset. We need data scientists and technology solutions to help us organize, segment, and learn from all that data so we can then apply our marketing know-how to communicate with the right people at the right time with the right message.
With this basic tenet in mind, understanding data science terminology, to be able to vet solutions and partners, is more crucial than ever to marketing success.
An Introduction to Artificial Intelligence
You can think of artificial intelligence, or AI, as the overarching term that describes a varied set of technologies or algorithms that can ingest data sets, learn from that data at speeds faster than human capacity, and then apply logic to perform a task or make a prediction based on that data set.
If you want a practical example of AI, you can look almost anywhere today, from Tesla’s self-driving cars to your Netflix account. Even Facebook employs AI to suggest who to tag in that photo you uploaded of your latest family gathering.
For our purposes, we’ll discuss two specific kinds of AI – machine learning and deep learning. Because these terms are related, they can often be confused or seem interchangeable, but in terms of healthcare’s AI capabilities, there are some distinct differences.
What is Machine Learning?
Machine learning is one kind of AI. Systems that employ this automated method of data analytics learn from large data sets and recognize patterns within them, with limited human intervention. In addition to pattern identification, machine learning can determine the cause and effect between dimensions of data and behaviors or outcomes. Using algorithms to parse data and learn from that data, machine learning can make informed decisions based on what it has learned.
Think about your Spotify or Pandora playlists and stations. In order for these services to choose among the entirety of new songs and artists to recommend something just for you, machine learning algorithms associate your individual preferences with other individual’s preferences who have similar musical taste.
What is Deep Learning?
Deep learning is machine learning but taken to the next level. Algorithms using deep learning are structured in layers to create an artificial neural network
, basically a brain, that can learn and make decisions on its own. Yes, both machine learning and deep learning are considered artificial intelligence, but deep learning is what powers the most human-like artificial intelligence. In fact, deep learning is the next evolution in machine learning, it removes the need for a programmer to tell a machine when and how to make an accurate decision.
Deep learning is possible because of the massive amounts of data generated today, and the innovation opportunities in deep learning are huge. In effect, with more data, deep learning technologies become more effective, which is where healthcare organizations and healthcare marketing teams can benefit the most.
AI for Healthcare
In healthcare, these advancements in artificial intelligence, machine learning, and deep learning provide the opportunity for data scientists to dramatically improve the accuracy of condition forecasting and the campaign targeting that rely on these models. AI is how a healthcare marketer will know if Felicity Craig will be interested in a mobile mammography service and whether she’s more likely to respond to a Facebook ad or a direct mail piece – times all your service lines and all your addressable audience. By combining deep learning technologies with consumer data, clinical and financial records, condition-specific information, online behavior, and more, the healthcare industry is poised to leverage a vast wealth of data to help people make better healthcare choices, catch conditions before they become critical, and improve the overall health of a population.Learn more
about Healthgrades' AI capabilities for our customer data platform (CDP) solution.