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

Aug. 21, 2017
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.

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.

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