A machine learning-based framework to identify type 2 diabetes through electronic health records

International Journal of Medical Informatics
Tao ZhengYou Chen

Abstract

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. 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-Nearest-Neighbors, Naïve 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...Continue Reading

Citations

Nov 27, 2018·Statistical Methods in Medical Research·Theodora S BrisimiIoannis Ch Paschalidis
Mar 29, 2018·BMC Medical Informatics and Decision Making·Weiqi ChenMei Liu
Nov 29, 2019·Healthcare Informatics Research·Shahabeddin AbhariAli Garavand
Jul 31, 2020·Journal of Medical Engineering & Technology·Ambaji S JadhavSunil Biradar
Dec 8, 2019·Systematic Reviews·Corrado LaneraIleana Baldi
Apr 6, 2018·Journal of the Royal Society, Interface·Travers ChingCasey S Greene
Jul 16, 2019·Journal of the American Medical Informatics Association : JAMIA·Ying XiongJun Yan
Jun 18, 2020·Alzheimer's & Dementia : Translational Research & Clinical Interventions·Donna TjandraJenna Wiens
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Aug 5, 2017·Cerebellum & Ataxias·Eva BolcekováRobert Rusina
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Related Concepts

Machine Learning
Bayesian Prediction
Diabetes Mellitus, Non-Insulin-Dependent
Pilot Projects
Logistic Regression
Genome-Wide Association Study
Electronic Health Records
Support Vector Machines
Diabetes
Diabetes Mellitus, Non-Insulin-Dependent

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