Will you get diabetes? University of Texas professor develops AI to make better predictions

You've just been diagnosed with prediabetes: Wouldn't you want to know if you were in danger of actually getting diabetes?

Wouldn't you want to know if the recommended intervention would actually benefit you, especially if the interventions are medications that cost money and could have side effects?

A University of Texas McCombs School of Business study used artificial intelligence to better predict whether someone with a prediabetes diagnosis is at risk for developing Type II diabetes.

Professor Maytal Saar-Tsechansky, who teaches information, risk and operations management at UT, developed the algorithm to predict which patients are most likely to benefit from prevention measures such as medication or one-to-one coaching, and which ones do not need such interventions because they are unlikely to develop diabetes.

How was the study done?

Saar-Tsechansky used the electronic health records of almost 90,000 people in Israel who had a prediabetes diagnosis from 2003 to 2012. The health records had data on body measures such as height weight, lab tests, disease diagnoses, prescriptions and demographics.

Then, Saar-Tsechansky used a computer to sort out who was given preventative medicine, such as metformin, and whether they developed diabetes or not.

Putting the algorithm to the test, Saar-Tsechansky estimates the computer learning would have prevented 25% more diabetes diagnoses than using the current Framingham diabetes risk score developed by the U.S. National Institutes of Health — a score many doctors use.

"These personalized predictions really leverage intricate details," she said. "It allows us to make more accurate predictions."

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Why use health records in Israel?

Saar-Tsechansky used Israeli data because of the information in the public health records. Countries that have universal health care systems like Israel are more likely to have a person's whole health history in their health records, she said, because there aren't frequent changes in health systems or changes in insurance carriers that people in the U.S. regularly experience.

Universal health systems, she said, "that's a treasure. At a population level, you can find patterns."

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How could this research impact public health?

Using machine-learning decision making could lead to cost savings and better health resources allocations because interventions wouldn't be wasted on people who were unlikely to become diabetic, Saar-Tsechansky said, and the right interventions could be targeted to the type of people who would respond well to those interventions.

"We need to be more proactive," Saar-Tsechansky said. When it comes to using medication to prevent diabetes, "we need to know who should we give it to and who shouldn't we."

There were some basic differences with the person who the AI model noted should have interventions and the clinical score indicated for interventions. In general, the machine identified people with a higher body mass index and people who were older.

Is it just for diabetes predictions?

What Saar-Tsechansky developed could be used for any disease using any set of health records, she said.

Even with the outcome of her study, Saar-Tsechansky said "any model, any machine learning is imperfect." It should be taken with the caveat that it can be off in both directions, either overestimating or underestimating a person's risk.

This article originally appeared on Austin American-Statesman: University of Texas professor develops AI for diabetes prediction