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

By Tao Zheng, W. Xie, Liling Xu, Xiaoying He, Ya Zhang, Mingrong You, Gong Yang, You Chen. 2016

324 citations recorded by the source corpus.

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-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 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.

Subfields: Artificial Intelligence, Genetics, Health Information Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Brett K. Beaulieu-Jones
2. Yifan Peng
3. Anshul B Kundaje
4. Avanti Shrikumar
5. Casey S. Greene
6. Yang Wang
7. Donald E. Brown
8. Amr M. Alexandari

In Genetics

1. Brett K. Beaulieu-Jones
2. Casey S. Greene
3. Yang Wang
4. Wei Xie
5. Donald E. Brown
6. Steven M. Boker
7. Zhiyong Lu
8. Jinbo Xu

In Health Information Management

1. Muhammad Bilal
2. Ala Al-Fuqaha
3. Junaid Qadir
4. Adnan Qayyum

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “This can be time consuming and expensive [90].”
• Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “The use of ML for the diagnosis of diabetes from EHRs is presented in [39].”
• Task-oriented Dialogue System for Automatic Diagnosis (2018, background): “Experimental re-sults prove the effectiveness of the identification of heart failure (Jonnalagadda et al., 2017; Choi et al., 2016), type 2 diabetes (Li et al., 2015; Zheng et al., 2017), autism spectrum disorders (Doshi-Velez et al., 2014), infection detection (Tou et al., 2018) etc.”
• PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers (2016, methodology): “We consider extending this novel approach to other machine learning models, such as other classifiers [Zheng et al., 2017], regressors, and deep learning [Beaulieu-Jones and Greene, 2016] that are increasingly popular in privacy-sensitive domains.”
• Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review (2023, unknown): “EHRs [95] 300 χ χ χ χ Experimental Research χ”
• Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, background): “[104] Diagnosis of type 2 diabetes DT; KNN; NBC LR; RF; SVM 300 average AUC = 98%”
• Predicting diabetes-related hospitalizations based on electronic health records (2018, methodology): “[12] Predictive methods, in particular, have been used for example in the context of heart-related problems,[13–15] hemodialysis,[16] diabetes in older adults,[17–20] and multiple disease prediction.”