machine learning based framework to identify type 2 diabetes hrough electronic health records
Objective: To discover diverse genotype phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome wide association study (GWAS) and phenome wide association study (PheWAS) more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g. via Electronic Health Records (EHR) However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. Materials and methods: We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely used machine learning models within our framework, including k NearestNeighbors, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects) randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. Results: We apply top performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state of the art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances ~0.98 in average AUC) which are much higher than the state of the art algorithm (0.71 in AUC) Discussion: Expert algorithm based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls. Conclusions: Our proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR. (c) 2016 Elsevier Ireland Ltd. All rights reserved. Correspondence to: 2525 West End Ave, Suite 1475, Department of Biomedical nformatics, Vanderbilt University, Nashville, TN 37203 USA. E mail address: you.chen@vanderbilt.edu (Y. Chen) ttp:/dx.doi.org/10.1016/j.ijmedinf.2016.09.014 386 5056/(c) 2016 Elsevier Ireland Ltd. All rights reserved. 1. Background and Significance Type 2 diabetes mellitus (T2DM) is a major disease with high penetrance in humans around the globe, a trend that is still on the rise [1,2] T2DM is a leading cause of morbidity and mortality and contributes to increased risks of heart disease by 2 to 4 times [1] A significant number of research investigations have been devoted to it, notably by means of genome wide association