Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records

Conclusions: We constructed accurate prediction models from EHR data using a hypothesis-free machine learning approach. Identification of established risk factors for T2D serves as proof of concept for this analytical approach, while novel factors selected by REFS represent emerging areas of T2D research. This methodology has potentially valuable downstream applications to personalized medicine and clinical research.

http://dst.sagepub.com/cgi/content/abstract/10/1/6?rss=1

Source: Journal of Diabetes Science and Technology - December 30, 2015 Category: Endocrinology Authors: Anderson, J. P., Parikh, J. R., Shenfeld, D. K., Ivanov, V., Marks, C., Church, B. W., Laramie, J. M., Mardekian, J., Piper, B. A., Willke, R. J., Rublee, D. A. Tags: Guest Editors: Riccardo Bellazzi, James Maisel, and Bharath Sudharsan Source Type: research