IBM, JDRF Collaborate to Apply Machine Learning to Type 1 Diabetes Research | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

IBM, JDRF Collaborate to Apply Machine Learning to Type 1 Diabetes Research

August 21, 2017
by Heather Landi
| Reprints

IBM and JDRF, a global organization funding type 1 diabetes research, are collaborating to develop and apply machine learning methods to analyze years of global type 1 diabetes research data and identify factors leading to the onset of the disease children.

Type 1 diabetes affects approximately 1.25 million Americans, and it currently does not have a cure. This research collaboration is expected to create an entry point for type 1 diabetes in the field of precision medicine, by combining JDRF’s connections to research teams around the globe and its subject matter expertise in diabetes research with the technical capability and computing power of IBM, according to a joint press release.

“At JDRF, we are absolutely committed to seeing a world without type 1 diabetes, and with this partnership, we’re adding some of the most advanced computing power in the world to our mission,” Derek Rapp, JDRF president and CEO, said. “JDRF supports researchers all over the world, but never before have we been able to analyze their data comprehensively, in a way that can tell us why some children who are at risk get T1D and others do not. IBM’s analysis of the existing data could open the door to understanding the risk factors of T1D in a whole new way, and to one day finding a way to prevent T1D altogether.”

IBM scientists plan to look across different data sets and apply machine learning algorithms to help find patterns and factors at play, with the goal of identifying ways that could delay or prevent type 1 diabetes in children. In order to match variables and data formats and compare the differing data sets, the scientists plan to leverage previously collected data from global research projects. Data analysis will explore the inclusion of genetic, familial, autoantibody and other variables to create a foundational set of features that is common to all data sets. The models that will be produced will quantify the risk for type 1 diabetes from the combined dataset using this foundational set of features.

As a result, JDRF says the organization will be in a better position to identify top predictive risk factors for type 1 diabetes, cluster patients based on top risk factors, and explore a number of data-driven models for predicting onset.

“Nearly 40,000 new cases of type 1 diabetes will be diagnosed in the U.S. this year. And each new patient creates new records and new data points that, if leveraged, could provide additional understanding of the disease,” Jianying Hu, senior manager and program director, Center for Computational Health at IBM Research, said in a statement. “The deep expertise our team has in artificial intelligence applied to healthcare data makes us uniquely positioned to help JDRF unlock the insights hidden in this massive data set and advance the field of precision medicine towards the prevention and management of diabetes.”

Future phases of the collaboration may consist of furthering the analysis of big data toward the goal of better understanding causes of type 1 diabetes.

IBM and JDRF, a global organization funding type 1 diabetes research, are collaborating to develop and apply machine learning methods to analyze years of global type 1 diabetes research data and identify factors leading to the onset of the disease children.

Type 1 diabetes affects approximately 1.25 million Americans, and it currently does not have a cure. This research collaboration is expected to create an entry point for type 1 diabetes in the field of precision medicine, by combining JDRF’s connections to research teams around the globe and its subject matter expertise in diabetes research with the technical capability and computing power of IBM, according to a joint press release.

“At JDRF, we are absolutely committed to seeing a world without type 1 diabetes, and with this partnership, we’re adding some of the most advanced computing power in the world to our mission,” Derek Rapp, JDRF president and CEO, said. “JDRF supports researchers all over the world, but never before have we been able to analyze their data comprehensively, in a way that can tell us why some children who are at risk get T1D and others do not. IBM’s analysis of the existing data could open the door to understanding the risk factors of T1D in a whole new way, and to one day finding a way to prevent T1D altogether.”

IBM scientists plan to look across different data sets and apply machine learning algorithms to help find patterns and factors at play, with the goal of identifying ways that could delay or prevent type 1 diabetes in children. In order to match variables and data formats and compare the differing data sets, the scientists plan to leverage previously collected data from global research projects. Data analysis will explore the inclusion of genetic, familial, autoantibody and other variables to create a foundational set of features that is common to all data sets. The models that will be produced will quantify the risk for type 1 diabetes from the combined dataset using this foundational set of features.

As a result, JDRF says the organization will be in a better position to identify top predictive risk factors for type 1 diabetes, cluster patients based on top risk factors, and explore a number of data-driven models for predicting onset.

“Nearly 40,000 new cases of type 1 diabetes will be diagnosed in the U.S. this year. And each new patient creates new records and new data points that, if leveraged, could provide additional understanding of the disease,” Jianying Hu, senior manager and program director, Center for Computational Health at IBM Research, said in a statement. “The deep expertise our team has in artificial intelligence applied to healthcare data makes us uniquely positioned to help JDRF unlock the insights hidden in this massive data set and advance the field of precision medicine towards the prevention and management of diabetes.”

Future phases of the collaboration may consist of furthering the analysis of big data toward the goal of better understanding causes of type 1 diabetes.

 

Get the latest information on Health IT and attend other valuable sessions at this two-day Summit providing healthcare leaders with educational content, insightful debate and dialogue on the future of healthcare and technology.

Learn More

Topics

News

Healthcare Execs Anticipate High Cost Returns from Predictive Analytics Use

Healthcare executives are dedicating budget to predictive analytics, and are forecasting significant cost savings in return, according to new research from the Illinois-based Society of Actuaries.

Adam Boehler Tapped by Azar to Serve as Senior Value-Based Care Advisor

Adam Boehler, currently director of CMMI, has also been named the senior advisor for value-based transformation and innovation, HHS Secretary Alex Azar announced.

Vivli Launches Clinical Research Data-Sharing Platform

On July 19 a new global data-sharing and analytics platform called Vivli was unveiled. The nonprofit group’s mission is to promote, coordinate and facilitate scientific sharing and reuse of clinical research data.

Survey: More Effective IT Needed to Improve Patient Safety

In a Health Catalyst survey, physicians, nurses and healthcare executives said ineffective information technology, and the lack of real-time warnings for possible harm events, are key obstacles to achieving their organizations' patient safety goals.

Physicians Still Reluctant to Embrace Virtual Tech, Survey Finds

While consumers and physicians agree that virtual healthcare holds great promise for transforming care delivery, physicians still remain reluctant to embrace the technologies, according to a new Deloitte Center for Health Solutions survey.

Geisinger, AstraZeneca Partner on Asthma App Suite

Geisinger has partnered with pharmaceutical company AstraZeneca to create a suite of products that integrate into the electronic health record and engage asthma patients and their providers in co-managing the disease.