Sorting out AI, ML, DL, and NLP

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Posted By: Roberta Mullin December 19, 2018

The alphabet soup of acronyms in the world Artificial Intelligence. What are they? What do they mean? What is the difference between them? This is our ongoing reporting on AI and how it is being integrated into healthcare technology. We are seeking out the thought leaders and innovations that are moving the needle forward using artificial intelligence. Read more posts on Artificial Intelligence in Healthcare.

Follow the hashtag #AIinHealthcare.

Watch

Operationalizing NLP to Support Value-based Care
Healthcare providers are facing an urgent need to streamline operations while improving quality of care and patient satisfaction. With a wealth of technology hype around AI, Natural Language Processing (NLP) and big data, how are providers to know what investments to make and how to bring these technologies into production use? Watch this webinar as Atrius Health (@atriushealth), and NLP experts, Linguamatics (@Linguamatics) explore the practical uses of NLP in healthcare and give real-life examples of how Atrius Health has implemented processes to improve clinical documentation, identify at-risk patients and streamline their ACO reporting.

To Read

In the News

Cala Health Streamlines Data and Operational Management for Neurological Device Study with Medidata Cloud
Medidata (@Medidata) announced that Cala Health, a neuromodulation platform company developing wearable therapies for chronic disease, is adopting the Medidata Cloud to centralize management of a large observational study.

Artificial Intelligence in Healthcare Market Worth $36.1 Billion by 2025 – Exclusive Report by MarketsandMarkets™
According to the new market research report “Artificial Intelligence in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-Aware Computing, Computer Vision), End-Use Application, End User, and Geography – Global Forecast to 2025″, published by MarketsandMarkets™ (@marketsmarkets), the Artificial Intelligence In Healthcare Market is estimated to be valued at USD 2.1 billion in 2018 and is expected to reach USD 36.1 billion by 2025, at a CAGR of 50.2% from 2018 to 2025.

New clinical study: Kaia Health’s AI app successfully decreases symptoms of Chronic Obstructive Pulmonary Disease
A peer-reviewed clinical study, published in the International Journal of Chronic Obstructive Pulmonary Disease, has shown to successfully reduce symptoms of Chronic Obstructive Pulmonary Disease (COPD) using a new artificial intelligence (AI) app, developed by leading digital therapeutics company Kaia Health (@KaiaHealth).

Artificial Intelligence Key to Improving Health Care Quality, Reducing Costs, Industry Leaders Say
Artificial Intelligence (AI) is key to building a better health care future, according to a recent survey of 500 U.S. health care leaders on their attitudes and usage of the technologies. Most (94 percent) responded that their organizations continue to invest in and make progress in implementing AI. The inaugural OptumIQ (@Optum) Annual Survey on AI in Health Care indicates a tipping point in the adoption of AI in the industry, estimating an average investment of $32.4 million per organization over the next five years.

Events

AI Solve: Healthcare – Webcast Replay
Recorded March 21, 2018 at UCSF in SanFrancisco
View the recording

AI is already beginning to shape the healthcare industry, Intel brought together some of the leading minds in the space. At the event called Intel SOLVE: Healthcare, in San Francisco, Intel brought together researchers from Harvard, Princeton, Stanford, GE Healthcare, Optum, Mayo Clinic, The MIT/Harvard Broad Institute and more to talk about the work they are doing with AI.

To Follow
The Basics and Resources

From the leading text book around the world, Artificial Intelligence: A Modern Approach.

Artificial Intelligence is composed of six different disciplines:

  1. Natural Language Processing to enable it to communicate successfully in English
  2. Knowledge Representation to store what it knows or hears
  3. Automated Reasoning to use the stored information to answer questions and to draw new conclusions
  4. Machine Learning to adapt to new circumstances and to detect and extrapolate patterns
  5. Computer Vision to perceive objects
  6. Robotics to manipulate objects and move about

To build a generally intelligent agent, you need machine learning in addition to the other aspects mentioned above.

Machine Learning is roughly the science of prediction. Given certain knowns (features), you wish to predict some unknowns (targets). The unknown could be structured (e.g. numeric) or unstructured (e.g. a string response).

Deep Learning is a sub field of machine learning where concepts are learned hierarchically. The simplest concepts emerge first, followed by more complicated concepts that build on the simpler ones. Usually, this leads to a simple layered hierarchy of concepts.

Optum Resource Library

Natural Language Processing: AI with an ROI
Health care providers need to see a return on any analytic investment they make. Natural language processing (NLP) is one way AI can help providers convert the potential within their health data into quality improvement and cost savings. Natural language processing is an AI technology that actually makes sense for health care.

IBM Analytics

Navigating the A.I. and Cognitive Maze – If you work in the area of Artificial Intelligence (AI) and Cognitive Computing, you might use buzz words and phrases which to others might be perceived as confusing jargon. This article attempts to explain what these terms mean, how they relate to one other and where they all fit along the AI and cognitive time continuum. I include a glossary of my top 20 useful AI/cognitive terms — and advice on getting started on your AI/cognitive journey.