Work

Type 2 diabetes risk forecasting from EMR data using machine learning.

Year: 2012

Type: article

Abstract: 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.

Source: PubMed Central

Authors: Subramani Mani, Yukun Chen, Tom Elasy, Warren Clayton, Joshua Denny

Institution: University of New Mexico

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of Diabetes and Digestive and Kidney Diseases

Award: Vanderbilt Center for Diabetes Translation Research