ArticleJournal of healthcare informatics research2020
Imputation of Missing Data in Electronic Health Records Based on Patients' Similarities.
Article in Journal of healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Dynamic Bayesian networks to predict loss of kidney function: a cross-institution use case in a large cohort with or at-risk of CKD.BMC medical informatics and decision making · 2026Article
- Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care.Frontiers in medicine · 2026Article
- Clustering-Informed Shared-Structure Variational Autoencoder for Missing Data Imputation in Large-Scale Healthcare Data.Statistics in medicine · 2025Article
- Data-driven discovery of medication effects on blood glucose from electronic health records.Patterns (New York, N.Y.) · 2025Article
- Identification of clinical phenotypes and heterogeneous treatment effects of surgical revascularization in ischaemic cardiomyopathy: a machine learning consensus clustering analysis.European heart journal. Digital health · 2025Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- MediSim: Multi-granular simulation for enriching longitudinal, multi-modal electronic health records.Patterns (New York, N.Y.) · 2025Article
- An exploratory study of electronic medical record implementation and recordkeeping culture: the case of hospitals in Indonesia.BMC health services research · 2025Article
- Methodologic Approach to Defining Comorbidities in a Cohort of Patients With Cancer: An Example in the Optimal Breast Cancer Chemotherapy Dosing Study.JCO clinical cancer informatics · 2025Article
- Predicting abatacept retention using machine learning.Arthritis research & therapy · 2025Observational
- On operational definitions of mortality.Kidney research and clinical practice · 2024Article
- Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.Health data science · 2024Review
- Evaluating the risk of endometriosis based on patients' self-assessment questionnaires.Reproductive biology and endocrinology : RB&E · 2023Article
- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
- Frequent temporal patterns of physiological and biological biomarkers and their evolution in sepsis.Artificial intelligence in medicine · 2023Article
- Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- An ensemble learning approach for diabetes prediction using boosting techniques.Frontiers in genetics · 2023Article
- Methylation risk scores are associated with a collection of phenotypes within electronic health record systems.NPJ genomic medicine · 2022Article
- Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches.Journal of diabetes and metabolic disorders · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Using electronic health records (EHR) as the source of data for mining and analysis of different health conditions has become an increasingly common approach. However, due to irregular observation times and other uncertainties inherent in medical settings, the EHR data sets suffer from a large number of missing values. Most of the traditional data mining and machine learning approaches are designed to operate on complete data. In this paper, we propose a novel imputation method for missing data to facilitate using these approaches for the analysis of EHR data. The imputation is based on a set of interpatient, multivariate similarities among patients. For a missing data point in a patient's lab results during his/her intensive care unit stay, the method ranks other patients based on their similarities with the ego patient in terms of lab values, then the missing value is estimated as a weighted average of the known values of the same laboratory test from other patients, considering their similarities as weights. A comparison of the estimated values by the proposed method with values estimated by several common and state-of-the-are methods, such as MICE and 3D-MICE, shows that the proposed method outperforms them and produces promising results.
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