Artificial intelligence can be helpful in diabetes monitoring, prevention

Artificial intelligence can be used to turn a blood glucose monitor (CGM) into a powerful diabetes monitoring tool.

Artificial intelligence can be used to turn a glucose monitor (CGM) into a powerful diabetes monitoring and prevention tool, suggests researchers at Klick Applied Sciences.

In findings presented at the NeurIPS conference in New Orleans, Klick scientists demonstrated how they used machine learning and just 12 hours of data from CGMs to determine whether a patient was prediabetic or diabetic.

“We’ve shown that 12 hours of monitoring can make a big difference in the lives of people at risk of developing diabetes, as long as it’s time to get it right,” said Jouhyun Jeon, study scientist and principal investigator at Klick Applied Sciences. “We think that CGMs can be used not only to monitor diabetes, but not at all.”

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For the study, approximately 600 patients who were healthy, known to be prediabetic, or living with Type 2 diabetes wore a CGM device for an average of 12 days. Physicists looked at glucose measurements over time and developed machine learning models to see if those values ​​could be used to determine whether that person was healthy, prediabetic, or diabetic.

Jeon said that they discovered that their 12-hour model showed similar accuracy of long-term results, correctly identifying two-thirds of patients with prediabetes, while also showing high accuracy in eating healthy patients and those with Type 2 diabetes. Jeon said the shorter framework is a big step, adding that most research takes 10 to 14 days to complete and often requires analysis by expert clinicians.

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According to the CDC, prediabetes is a serious health condition where blood sugar levels are higher than normal, but not high enough to be diagnosed as Type 2 diabetes. Approximately 96 million American adults – more than one in three – have prediabetes. More than 80 percent of those with prediabetes are unaware of it.

“Most people with advanced diabetes are not aware of their condition and do not consult a doctor until their ability to control blood sugar levels is irreparably damaged,” said Michael Lieberman, director of research and management development at Klick. Journal of Sciences “Our research has tremendous potential to help move digital blood glucose biomarkers into a position where they can be an invaluable tool for doctors to prevent diabetes before it starts.”

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These findings are the latest in Klick’s ongoing work in the diabetes space. Their “Homeostasis as a proportional-integral control system” study, based on mathematical models for some underlying changes in how glucose is regulated that can cause diabetes, was published in Nature in 2020. Earlier they also brought findings before the 2018 International. Joint Conference on Artificial Intelligence (IJCAI) in Stockholm, Sweden.


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