{"id":51507,"date":"2022-12-02T16:02:09","date_gmt":"2022-12-02T15:02:09","guid":{"rendered":"https:\/\/pharma-trend.com\/en\/scientists-develop-12-hour-method-to-predict-diabetes-onset-in-patients-using-artificial-intelligence\/"},"modified":"2022-12-02T16:02:09","modified_gmt":"2022-12-02T15:02:09","slug":"scientists-develop-12-hour-method-to-predict-diabetes-onset-in-patients-using-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/pharma-trend.com\/en\/scientists-develop-12-hour-method-to-predict-diabetes-onset-in-patients-using-artificial-intelligence\/","title":{"rendered":"Scientists Develop 12-Hour Method to Predict Diabetes Onset in Patients Using Artificial Intelligence"},"content":{"rendered":"
\n

\nNew Klick Applied Sciences research presented at leading AI conference<\/i>\n<\/p>\n

NEW YORK & TORONTO–(BUSINESS WIRE)–#AI<\/a>–Scientists at Klick Applied Sciences<\/a> have discovered a way to transform a continuous glucose monitor (CGM) into a powerful diabetes screening and prevention tool using artificial intelligence.\n<\/p>\n

<\/a><\/p>\n

\nIn findings presented Friday at the NeurIPS<\/a> conference in New Orleans, Klick scientists revealed how they used machine learning and just 12 hours of data from CGMs to determine whether a patient was prediabetic or diabetic.\n<\/p>\n

\n\u201cWe have demonstrated that 12 hours of monitoring can make a big difference in the lives of people at risk of developing diabetes while there\u2019s still time to course correct,\u201d said Jouhyun Jeon, lead scientist of the study and principal investigator at Klick Applied Sciences. \u201cWe think CGMs could be used to not just monitor diabetes\u2013but to prevent it altogether.\u201d\n<\/p>\n

\nFor the study, about 600 patients who identified as healthy, prediabetic, or living with Type 2 diabetes wore a CGM device for an average of 12 days. The scientists looked at their 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.\n<\/p>\n

\nJeon said they discovered their 12-hour model showed similar high accuracy to results from the longer intervals, correctly identifying two-thirds of patients with prediabetes, while also showing high accuracy in identifying healthy patients and those with Type 2 diabetes. Jeon said the shorter time frame is a big step forward, adding most research draws from 10 to 14 days worth of readings, and often requires analysis from expert clinicians.\n<\/p>\n

\nAccording to the CDC<\/a>, prediabetes is a serious health condition where blood sugar levels are higher than normal, but not high enough yet to be diagnosed as Type 2 diabetes. Approximately 96 million American adults\u2014more than one in three\u2014have prediabetes. Of those with prediabetes, more than 80 per cent don\u2019t know they have it.\n<\/p>\n

\n\u201cAn overwhelming majority of people with early-onset diabetes are not aware of their condition and don\u2019t consult a physician until their ability to control their blood sugar levels is irreparably damaged,\u201d said Michael Lieberman, managing director of research and development at Klick Applied Sciences. \u201cOur research has tremendous potential to help move blood glucose digital biomarkers into a position where they can be an invaluable tool for physicians for preventing diabetes before it starts.\u201d\n<\/p>\n

\nThese findings are the latest in Klick\u2019s ongoing work in the diabetes space. Their \u201cHomeostasis as a proportional\u2013integral control system\u201d study, <\/i>based on mathematical modeling to determine some of the underlying changes in how glucose is regulated that can cause diabetes, was published in Nature<\/i> in 2020. They also presented earlier findings at the 2018 International Joint Conference on Artificial Intelligence (IJCAI) in Stockholm, Sweden.\n<\/p>\n

\nAbout Klick Applied Sciences
\n
<\/b>Klick Applied Sciences<\/a>\u2019 diverse team of data scientists, engineers, and biological scientists conducts scientific research and develops AI\/ML and software solutions as part of the company\u2019s work to support commercial efforts using its proven business, scientific, medical, and technological expertise. Its 2019 Voice Assistants Medical Name Comprehension study laid the scientific foundation for rigorously testing voice assistant consumer devices in a controlled manner. Additional research in peer-reviewed journals, include \u201cThe need for artificial intelligence in digital therapeutics<\/i><\/a>,\u201d \u201cConsensus study of risk factors and symptoms of SARS-CoV-2 (COVID-19) using biomedical literature and social media data<\/i><\/a>,\u201d and \u201cCOVID-19 has inspired global healthcare innovation<\/i><\/a>.\u201d\n<\/p>\n

\nAbout Klick Group
\n
<\/b>The Klick Group of companies\u2013Klick Health (including Klick Katalyst), Klick Media Group, Klick Applied Sciences (including Klick Labs), Klick Consulting, Klick Ventures, and Sensei Labs\u2013is an ecosystem of brilliant talent collectively working to maximize their people\u2019s and clients\u2019 full potential. Established in 1997, Klick has teams across North America, with offices in New York, Philadelphia, and Toronto, and is opening global offices in Basel, Buenos Aires, London, Munich, Paris, Singapore, S\u00e3o Paulo, and Tokyo. Klick has consistently been named a Best Managed Company and Great Place to Work. In 2021, the company was recognized with 15 Best Workplace awards, including Best Workplaces for Women, Best Workplaces for Inclusion, Best Workplaces for Giving Back, Fast Company<\/i>\u2019s Best Workplaces for Innovators, and FORTUNE<\/i>\u2019s Best Workplaces for Millennials.\n<\/p>\n

\nFor more information, or a copy of the abstract, please contact Amanda Ferguson at pr@klick.com<\/a> or 416-214-4977.\n<\/p>\n

Contacts<\/b> <\/p>\n

\nAmanda Ferguson
\n
pr@klick.com<\/a> or 416-214-4977\n<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

New Klick Applied Sciences research presented at leading AI conference NEW YORK & TORONTO–(BUSINESS WIRE)–#AI–Scientists at Klick Applied Sciences have discovered a way to transform a continuous glucose monitor (CGM) into a powerful diabetes screening and prevention tool using artificial intelligence. In findings presented Friday at the NeurIPS conference in New Orleans, Klick scientists revealed … [Read more…]<\/a><\/span><\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":{"0":"entry","1":"post","2":"publish","3":"author-business","4":"post-51507","6":"format-standard","7":"category-industry"},"yoast_head":"\nScientists Develop 12-Hour Method to Predict Diabetes Onset in Patients Using Artificial Intelligence - Pharma Trend<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/pharma-trend.com\/en\/scientists-develop-12-hour-method-to-predict-diabetes-onset-in-patients-using-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Scientists Develop 12-Hour Method to Predict Diabetes Onset in Patients Using Artificial Intelligence - Pharma Trend\" \/>\n<meta property=\"og:description\" content=\"New Klick Applied Sciences research presented at leading AI conference NEW YORK & TORONTO–(BUSINESS WIRE)–#AI–Scientists at Klick Applied Sciences have discovered a way to transform a continuous glucose monitor (CGM) into a powerful diabetes screening and prevention tool using artificial intelligence. 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