Causal inference is especially important in AI prescribed medical treatments
The Yuan
November 22, 2023
In this sixth installment of his ongoing series: AI Prediction, AI Prescription and Causation in Medicine, CUNY Data Science Prof Scott Burk explains the concept of causal inference and pinpoints the key reasons for its critical importance in the realm of AI and its applications.
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Tags: Analytics, AI, Leadership
Turning predictive models into prescriptive ones is a meticulous process
The Yuan
September 01, 2023
The previous article concluded with three considerations for transforming a predictive model into a prescriptive one. This one will explain each of these in greater depth. First, one should always assume causation based on well-documented scientific studies. Second, make sure to design experiments and randomized clinical trials that can actually establish causation. Third, think about causal inference methods that may include Bayesian networks. These methods will also be discussed in future installments.
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Tags: Analytics, AI, Leadership
The future of AI is bright if humans and machines work hand-in-hand
The Yuan
June 30, 2023
AUSTIN, TEXAS - Advancements in artificial intelligence (AI) are proceeding at an incredible rate. AI was once an arcane topic limited to researchers, academics, and high-tech companies, but has now gone mainstream. With the rapidly increasing technical sophistication of AI chatbots like OpenAI’s ChatGPT-4, Google’s Bard, and others, the news has been full of predictions ranging from the detrimental effects of AI on the workforce to the tremendous benefits society will receive from them. This article explores a few truths about these technologies and some predictions on how things might shake out in the near future.
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Tags: Analytics, AI, Leadership
Causation in AI applications in medicine, predictions, and prescriptions
The Yuan
April 03, 2023
This paradigm is attributed to Judea Pearl1 in his work The Book of Why.2 Pearl described a ‘ladder of causation,’ where increasing evidence provides insight into one’s understanding of why something occurred and with what level of deterministic mechanism behind it.
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Tags: Analytics, AI, Leadership
Causation: The most misunderstood concept in AI
The Yuan
February 21, 2023
AUSTIN, TEXAS - Artificial intelligence (AI) applications in healthcare are improving clinical decision making and therapies and enhancing clinical and operational efficiencies. They offer the potential to radically transform medical practice and improve global health. While this is great news, the misapplication of AI models can easily lead to undesirable results: To avoid this, the formulation and application of AI models require different philosophical and mathematical assumptions.
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Tags: Analytics, AI, Leadership
Models, Models, Models
LinkedIn
November 08, 2022
There is a great deal of talk about models! AI models, machine learning models, statistical models and more. We will be using this in a very general sense in this series, but what is a model? We deal with models unconsciously, all the time. Without them we would have a hard time navigating through life. A model is just a representation of a mental concept. We provide three diverse examples. First, take a look at the following:
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Tags: Analytics, AI, Leadership
Some Fundamentals – Process, Data and Models
LinkedIn
November 01, 2022
In the last post we considered shortcomings of existing analytics programs – limitations and gaps in culture and organizations. We discussed building a successful analytics program by justifying and selling the concept throughout the organization. Then avoiding gaps by redesigning or designing the program for success. In this post we move into the basics that are shared across different data-driven techniques before we launch into upcoming psots where we cover each topic independently and more in-depth.
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Tags: Analytics, AI, Leadership
The Goal – 7 Keys to Enable Everyone for AI with a Platform
LinkedIn
August 25, 2022
I had a great conversation with a longtime (almost 20 year) friend. Doug Bryan is an AI strategist with a great career history in AI and ML (like Amazon, Stanford, Accenture, Merkle, many more). We met at Overstock . Much of the conversation was about his role as AI strategist and what is happening with technology and in the marketplace. However, Doug started speaking to something that immediately caught my ear. I don’t think he had formally thought through all of it and it intrigued me. So, I had him elaborate and it boiled down to 7 points for enabling everyone for AI. Here are the components needed to make this happen:
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Tags: Analytics, AI, Predictive Analytics
It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690)
Francis and Taylor, CRC Press
July 09, 2020
Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology?
This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.
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Tags: AI, Analytics, Predictive Analytics