Chronic Kidney Disease stratification using office visit records: Handling data imbalance via hierarchical meta-classification

Moumita Bhattacharya,Hagit Shatkay,Claudine Jurkovitz

doi:10.1186/s12911-018-0675-x

Moumita Bhattacharya, Hagit Shatkay + Show 1 more

Open Access

https://doi.org/10.1186/s12911-018-0675-x

Copy DOI

Abstract

BackgroundChronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself. In its advanced forms it carries severe outcomes and can lead to death. It is thus important to detect the disease as early as possible, which can help devise effective intervention and treatment plan.Here we investigate ways to utilize information available in electronic health records (EHRs) from regular office visits of more than 13,000 patients, in order to distinguish among several stages of the disease. While clinical data stored in EHRs provide valuable information for risk-stratification, one of the major challenges in using them arises from data imbalance. That is, records associated with a more severe condition are typically under-represented compared to those associated with a milder manifestation of the disease. To address imbalance, we propose and develop a sampling-based ensemble approach, hierarchical meta-classification, aiming to stratify CKD patients into severity stages, using simple quantitative non-text features gathered from standard office visit records.MethodsThe proposed hierarchical meta-classification method frames the multiclass classification task as a hierarchy of two subtasks. The first is binary classification, separating records associated with the majority class from those associated with all minority classes combined, using meta-classification. The second subtask separates the records assigned to the combined minority classes into the individual constituent classes.ResultsThe proposed method identifies a significant proportion of patients suffering from the more advanced stages of the condition, while also correctly identifying most of the less severe cases, maintaining high sensitivity, specificity and F-measure (≥ 93%). Our results show that the high level of performance attained by our method is preserved even when the size of the training set is significantly reduced, demonstrating the stability and generalizability of our approach.ConclusionWe present a new approach to perform classification while addressing data imbalance, which is inherent in the biomedical domain. Our model effectively identifies severity stages of CKD patients, using information readily available in office visit records within the realistic context of high data imbalance.

Highlights

Chronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself
The high-level of performance of our model demonstrates that patient information that are routinely collected during office visits form a sound basis for CKD risk stratification
The dataset consists of 120,739 records comprising patient information stored in the Electronic Health Record (EHR); records were included in the dataset if the corresponding patient was diagnosed with CKD stage 3 or higher during any follow-up visit

Summary

Introduction

Chronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself. Chronic kidney disease (CKD) is defined as kidney damage persisting for more than three months It is currently affecting about 15% of the adult population in the US, accompanied by co-morbidities and associated with increased mortality rates [1]. The disease is typically classified into five stages, 1–5, indicating increasing order of severity [2]. These severity stages are clinically quantified through the use of the Estimated glomerular filtration rate (eGFR), an indicator of the level of kidney function.. As chronic kidney disease – even in its advanced stages – is often asymptomatic, the relevant lab tests are not typically ordered and many CKD patients go undiagnosed [3]. Patients who remain under-treated, especially in stages 4 and 5, are at high risk for end-stage renal disease and death

Objectives

Methods

Results

Discussion

Conclusion