OBJECTIVE: To test the feasibility of using data collected in electronic medical records for development of effective models for diabetes risk forecasting. METHODS: Using available demographic, clinical and lab parameters of more than two thousand patients from Electronic medical records, we applied different machine learning algorithms to assess the risk of development of type 2 diabetes (T2D) six months to one year later. RESULTS: We achieved an AUC greater than 0.8 for predicting type 2 diabetes 365 days and 180 days prior to diagnosis of diabetes. CONCLUSION: Diabetes risk forecasting using data from EMR is innovative and has the potential to identify, automatically, high-risk populations for early intervention with life style modifications such as diet and exercise to prevent or delay the development of T2D. Our study shows that T2D risk forecasting from EMR data is feasible.
OBJECTIVE: To test the feasibility of using data collected in electronic medical records for development of effective models for diabetes risk forecasting. METHODS: Using available demographic, clinical and lab parameters of more than two thousand patients from Electronic medical records, we applied different machine learning algorithms to assess the risk of development of type 2 diabetes (T2D) six months to one year later. RESULTS: We achieved an AUC greater than 0.8 for predicting type 2 diabetes 365 days and 180 days prior to diagnosis of diabetes. CONCLUSION:Diabetes risk forecasting using data from EMR is innovative and has the potential to identify, automatically, high-risk populations for early intervention with life style modifications such as diet and exercise to prevent or delay the development of T2D. Our study shows that T2D risk forecasting from EMR data is feasible.
Authors: G F Cooper; C F Aliferis; R Ambrosino; J Aronis; B G Buchanan; R Caruana; M J Fine; C Glymour; G Gordon; B H Hanusa; J E Janosky; C Meek; T Mitchell; T Richardson; P Spirtes Journal: Artif Intell Med Date: 1997-02 Impact factor: 5.326
Authors: Benjamin A Goldstein; Michael J Pencina; Maria E Montez-Rath; Wolfgang C Winkelmayer Journal: J Am Med Inform Assoc Date: 2016-06-29 Impact factor: 4.497
Authors: Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis Journal: J Am Med Inform Assoc Date: 2016-05-17 Impact factor: 4.497
Authors: Tao Zheng; Wei Xie; Liling Xu; Xiaoying He; Ya Zhang; Mingrong You; Gong Yang; You Chen Journal: Int J Med Inform Date: 2016-10-01 Impact factor: 4.046
Authors: Ariana E Anderson; Wesley T Kerr; April Thames; Tong Li; Jiayang Xiao; Mark S Cohen Journal: J Biomed Inform Date: 2015-12-17 Impact factor: 6.317