Increasing the Density of Laboratory Measures for Machine Learning Applications

Vida Abedi,Ramin Zand,Manu K Shivakumar,Mohammed Yeasin,Ming Ta Michael Lee,Donna M Wolk,Matthew J Shellenberger,Venkatesh Avula,Durgesh P Chaudhary,Yanfei Zhang,Josep Bassaganya-Riera,Raquel Hontecillas,Jiang Li,Harshit S Khara

doi:10.3390/jcm10010103

Abstract

Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications. Method. We analyzed the laboratory measures derived from Geisinger’s EHR on patients in three distinct cohorts—patients tested for Clostridioides difficile (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns. Results. We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for C. difficile infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as −35.5 for the Cdiff, −8.3 for the IBD, and −11.3 for the OA dataset. Conclusions. An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.

Highlights

Given the complexity and high dimensionality of Electronic Health Records (EHR), the need for imputation is an inevitable aspect in any study that attempts to use such data for downstream analysis or building advanced machine learning models for decision support systems for clinical applications
We identified a total of 67,445 patients in three different cohorts (Cdiff, OA, and Inflammatory Bowel Disease (IBD))
We identified a total of 46,215 patients tested for C. difficile

Summary

Introduction

Given the complexity and high dimensionality of Electronic Health Records (EHR), the need for imputation is an inevitable aspect in any study that attempts to use such data for downstream analysis or building advanced machine learning models for decision support systems for clinical applications. The level and extent of the missing values in healthcare systems are typically not at random. Three main categories explain the missingness in clinical settings [2,3]—incompleteness, inconsistency, and inaccuracy—and these can capture a variety of situations, including the following: the patient could have been cared for outside of the healthcare system where the data are collected, the patient did not seek treatment, the health care provider did not enter the information, the patient expired, and the missing value was not needed. The development of an adaptive imputation strategy designed for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications

Methods

Results

Conclusion