Imputation of missing values for electronic health record laboratory data

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Artificial Intelligence Transforms the Future of Health Care

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Rebutting Existing Misconceptions About Multiple Imputation as a Method for Handling Missing Data

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Comparison of imputation methods for missing laboratory data in medicine

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Deep learning and alternative learning strategies for retrospective real-world clinical data

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Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry

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Using Machine Learning to Predict Laboratory Test Results

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Novel Screening Tool for Stroke Using Artificial Neural Network

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3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data

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Flexible Imputation of Missing Data, 2nd ed.

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Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record

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Machine learning models to predict disease progression among veterans with hepatitis C virus

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Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

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Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data

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Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

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Evaluation of two-fold fully conditional specification multiple imputation for longitudinal electronic health record data

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A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients

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Early Detection of Septic Shock Onset Using Interpretable Machine Learners

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Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

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Obesity and mortality after the first ischemic stroke: Is obesity paradox real?

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Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models

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Predicting short and long-term mortality after acute ischemic stroke using EHR

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Consistency, bias and efficiency of the normal-distribution-based MLE: The role of auxiliary variables

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Trends in ischemic stroke outcomes in a rural population in the United States.

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Polygenic Risk Scores Augment Stroke Subtyping

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A functional HTR1A polymorphism, rs6295, predicts short-term response to lurasidone: confirmation with meta-analysis of other antipsychotic drugs

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Comparisons of Methods for Analysis of Repeated Binary Responses with Missing Data

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Increasing the Density of Laboratory Measures for Machine Learning Applications

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Imputation strategies for blood pressure data nonignorably missing due to medication use

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Application of Gibbs Sampling to Nested Variance Components Models with Heterogeneous Within-Group Variance

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Multiple imputation of missing blood pressure covariates in survival analysis

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Multimodal biomedical AI

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Multimodal data fusion for cancer biomarker discovery with deep learning

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Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation

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Designing a feature selection method based on explainable artificial intelligence

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Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models

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Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview

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Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records

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Artificial Intelligence–Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review

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A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets

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An open-source framework for end-to-end analysis of electronic health record data

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Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records

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Minimizing bias when using artificial intelligence in critical care medicine

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Development and external validation of temporal fusion transformer models for continuous intraoperative blood pressure forecasting

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Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction

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Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation

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Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review

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Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

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Opportunities and challenges for biomarker discovery using electronic health record data

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Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment

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Methodological issues of the electronic health records’ use in the context of epidemiological investigations, in light of missing data: a review of the recent literature

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Artificial Intelligence and Machine Learning in Transfusion Practice: An Analytical Assessment

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Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare

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Risk prediction of heart diseases in patients with breast cancer: A deep learning approach with longitudinal electronic health records data

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Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

Abedi, V., Razavi, S., Khan, A., Avula, V., Tompe, A. P., Poursoroush, A., Sadr, A. V., Li, J., & Zand, R. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10235710 · Cited by 16 · see citing works

Opening the Precision Diabetes Care through Digital Healthcare

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A comprehensive survey of imputation methods in medical missing data analysis

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Data Imputation in Healthcare Applications

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Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study

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Incorporating informatively collected laboratory data from EHR in clinical prediction models

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Common clinical blood and urine biomarkers for ischemic stroke: an Estonian Electronic Health Records database study

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Studying missingness in spinal cord injury data: challenges and impact of data imputation

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Imputation Strategies Under Clinical Presence: Impact on Algorithmic FairnessPreprint

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A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation

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Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm

Jin, W., Hao, W., Shi, X., Fritsche, L. G., Salvatore, M., Admon, A. J., Friese, C. R., & Mukherjee, B. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12237313 · Cited by 8 · see citing works

Development and validation of a Multi-Causal investigation and discovery framework for knowledge harmonization (MINDMerge): A case study with acute kidney injury risk factor discovery using electronic medical records

Zhang, M., Zhang, X., Dai, M., Wu, L., Liu, K., Wang, H., Chen, W., Liu, M., & Hu, Y. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105588 · Cited by 8 · see citing works

Analysis of missing data in electronic health records of people with diabetes in primary care in Spain: A population-based cohort study

Quesada, J. A., & Orozco‐Beltrán, D. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105722 · Cited by 8 · see citing works

Artificial intelligence in the clinical setting

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Predicting mortality among ischemic stroke patients using pathways-derived polygenic risk scores

Li, J., Chaudhary, D. P., Griessenauer, C. J., Carey, D. J., Zand, R., & Abedi, V. (2022) · Scientific Reports · doi:10.1038/s41598-022-16510-x · Cited by 7 · see citing works

Increasing efficiency of SVMp+ for handling missing values in healthcare prediction

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Machine Learning-Based Prediction of Stroke in Emergency Departments

Abedi, V., Misra, D., Chaudhary, D. P., Avula, V., Schirmer, C. M., Li, J., & Zand, R. (2024) · Therapeutic Advances in Neurological Disorders · doi:10.1177/17562864241239108 · Cited by 7 · see citing works

Defining the Age of Young Ischemic Stroke Using Data-Driven Approaches

Abedi, V., Lambert, C., Chaudhary, D., Rieder, E., Avula, V., Hwang, W., Li, J., & Zand, R. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12072600 · Cited by 7 · see citing works

Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction

Liao, W., Zhu, Y., Zhang, Z., Wang, Y., Wang, Z., Chu, X., Wang, Y., & Ma, L. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709166 · Cited by 8 · see citing works

How Deep is Your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation

Qian, L., Ellis, H. L., Wang, T., Wang, J., Mitra, R., Dobson, R., & Ibrahim, Z. M. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3568778 · Cited by 6 · see citing works

A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study

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AI for climate change: unveiling pathways to sustainable development through greenhouse gas emission predictions

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From Data to Decision: Standardization, Validation, and Regulatory Readiness of AI-Driven Analytical Chemistry Workflows

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Non-experimental rapid identification of lower respiratory tract infections in patients with chronic obstructive pulmonary disease using multi-label learning

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Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation

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Reducing Information and Selection Bias in EHR-Linked Biobanks via Genetics-Informed Multiple Imputation and Sample WeightingPreprint

Salvatore, M., Kundu, R., Du, J., Friese, C. R., Mondul, A. M., Hanauer, D. A., Lu, H., Pearce, C. L., & Mukherjee, B. (2024) · medRxiv · doi:10.1101/2024.10.28.24316286 · Cited by 8 · see citing works

Enhancing early gestational diabetes mellitus prediction with imputation-based machine learning framework: A comparative study on real-world clinical records

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Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study

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Routine HbA1c monitoring and cardiovascular outcomes in diabetes: Evidence from a large Spanish cohort

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Children’s Physical Activity during COVID-19 in Ontario, Canada: Parents’ Perspectives

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Patient Event Sequences for Predicting Hospitalization Length of Stay

Hansen, E. R., Nielsen, T. D., Mulvad, T., Stausholm, M. N., Sagi, T., & Hose, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34344-5_7 · Cited by 4 · see citing works

Prediction of postoperative infections by strategic data imputation and explainable machine learning

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Wearable device-measured circadian rest–activity rhythm with mortality risk in patients with cancer

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A Hybrid LSTM–Attention Model for Multivariate Time Series Imputation: Evaluation on Environmental Datasets

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Computational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference

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Curating a knowledge base for individuals with coinfection of HIV and SARS-CoV-2: a study protocol of EHR-based data mining and clinical implementation

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Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates

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Focal Loss Improves Performance of High-Sensitivity C-Reactive Protein Imbalanced Classification

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A Machine Learning Analysis of Physiological Monitoring Signals to Detect Small Airway Narrowing Due to Cold Air Exposure in Asthma

Chavoshian, S., Fossat, Y., Cao, X., Kaufman, J. M., Stanbrook, M. B., Tarlo, S. M., & Yadollahi, A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3582739 · Cited by 2 · see citing works

A Hybrid LSTM-Attention Approach for Missing Data Imputation in IoT Time Series

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Missing Value Imputation in Tabular Data Lakes Unleashed: A Hybrid Approach

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The health-related quality of life among stroke survivors with post-COVID conditions living in the United States

Hejazian, S. S., Vemuri, A. K., Sadr, A. V., Shahjouei, S., Bahrami, S., Abedi, V., & Zand, R. (2025) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2025.108246 · Cited by 2 · see citing works

Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data

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A Self-Supervised Framework for Laboratory Data Imputation in Electronic Health Records

Heilbroner, S. P., Carter, C., Vidmar, D. M., Mueller, E. T., Stumpe, M. C., & Miotto, R. (2025) · Communications Medicine · doi:10.1038/s43856-025-00973-w · Cited by 1 · see citing works

Visual and Anatomical Outcomes of a Single Intravitreal Dexamethasone in Diabetic Macular Edema: An 8 Year Real-World Study

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lab: an R package for generating analysis-ready data from laboratory records

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Disparities in perceived general health of stroke survivors in the United States

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Advances in Artificial Intelligence-Based Radiomultiomics for Assisting Cancer Diagnosis and Treatment

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Five-year dementia prediction and decision support system based on real-world data

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Integrated analysis for electronic health records with structured and sporadic missingness

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Improving the assessment of discharge fitness in an acute medical unit using wearable sensor data through machine learning

Garssen, S. H., Wesselink, S. F. O., Doggen, C. J. M., Koning, M. V., Veldkamp, B. P., & Haeri, M. A. (2025) · Intelligent Medicine · doi:10.1016/j.imed.2025.12.010 · Cited by 1 · see citing works

A multi-task learning approach for prediction of missing bioactivity values of compounds for the SLC transporter superfamily

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Temporal and comorbidity-aware representation of longitudinal patient trajectories from electronic health records

Sreenivasan, M., Madhavendranath, S., & Chacko, A. M. (2026) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae38de · Cited by 1 · see citing works

Knowledge-enhanced AI models for domain-specific application: a survey

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Multiple Imputation of a Continuous Outcome with Fully Observed Predictors Using TabPFN

Sepin, J. (2026) · Stats · doi:10.3390/stats9020038 · Cited by 1 · see citing works

Towards Explainable Hospital Mortality Prediction Using Critical Care Data

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Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness

Giesa, N., Zhumagambetov, R., Sekutowitcz, M., Piper, S. K., Sikora, V., Balzer, F., Haufe, S., & Boie, S. D. (2026) · Nature Communications · doi:10.1038/s41467-026-77303-8 · Cited by 1 · see citing works

Predicting whether patients in an acute medical unit are physiologically fit-for-discharge using machine learning: A proof-of-concept

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Similarity-Based Prediction of Ejection Fraction in Heart Failure Patients

Wallis, J., Azqueta-Gavaldón, A., Ananthakumar, T., Dürichen, R., & Albergante, L. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101035

A recurrent neural network model predicts activated partial thromboplastin time after treatment with heparin - a retrospective study (Preprint)Preprint

Boie, S. D., Engelhardt, L. J., Coenen, N., Giesa, N., Rubarth, K., Menk, M., & Balzer, F. (2022) · doi:10.2196/preprints.39187

Leveraging Established Machine Learning Methodology to Tackle Viral Infections in Emergency Departments: A Retrospective Analysis using Hemocytometric Data to detect SARS-CoV-2Preprint

Curier, L., Brouwer, C. G., wardak, H., van Twist, D. J. L., Bromuri, S., Martens, R. J. H., & Leers, M. P. G. (2023) · Research Square · doi:10.21203/rs.3.rs-3181908/v1

Thoughts and Considerations of Studies on Smart Devices for Cerebrovascular Disease Detection and Progression Monitoring

Li, J., Abedi, V., & Zand, R. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.06.009

Representing Health Data and Medical Knowledge for Deep Learning

Hansen, E. R. (2023) · doi:10.54337/aau621651364

Data analytics framework for sparse longitudinal structured biomedical data

Sixtová, I., Skopal, T., & Uher, T. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385940

A Preliminary Study on the Impact of Lower-Division Mathematics Courses on Student Success in Engineering

Çabuk, U. C., & Öztürk, Y. (2024) · ASEE Pacific Southwest Section Conference (PSW) · doi:10.18260/1-2--46017

Prediction of postoperative infections by strategic data imputation and explainable machine learningPreprint

Beldi, G., Guillen-Ramirez, H., Sánchez‐Taltavull, D., Perrodin, S. F., Triep, K., & Endrich, O. (2024) · Research Square · doi:10.21203/rs.3.rs-4594014/v1

Transforming Medical Practice: Harnessing the Power of Big Data and Machine Learning for Predictive Precision Medicine

Theocharopoulos, P. C., Bersimis, S., Georgakopoulos, S. V., Plagianakos, V. P., & Tasoulis, S. K. (2025) · doi:10.1109/cihm64979.2025.10969489

A probabilistic approach for building disease phenotypes across electronic health records

Vidmar, D., Freitas, J. D., Thompson, W., Pfeifer, J. M., Fornwalt, B. K., Zimmerman, N., Miotto, R., & Chen, R. (2025) · BioData Mining · doi:10.1186/s13040-025-00454-9

Comparison of Imputation Strategies for Incomplete Electronic Health Data

Zhang, S., Zhang, Z., Zhou, Y., Hong, S., & Liu, H. (2026) · Health Data Science · doi:10.34133/hds.0493

Enhanced multi-task learning of imputation and prediction via feature relationship graph learningPreprint

Hur, S.-W., Lee, T.-H., & Jun, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5473084

Clustering‐Informed Shared‐Structure Variational Autoencoder for Missing Data Imputation in Large‐Scale Healthcare Data

Charvadeh, Y. K., Seier, K. P., Panageas, K. S., Vaithilingam, D., Gönen, M., & Chen, Y. (2025) · Statistics in Medicine · doi:10.1002/sim.70335

A New Robust Imputation Method for Longitudinal Data with Non-Normal Continuous Outcomes

Darwish, N. M., Mohamed, Y. A., Gad, A. M., Abd-Rabou, A.-E. S., & Ibrahim, W. M. (2025) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2025.14.70

Multimorbidity measurement strategies for predicting hospital visits

Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z

Increasing the Density of Laboratory Measures for Machine Learning Applications

Abedi, V., Li, J., Shivakumar, M., Avula, V., Chaudhary, D. P., Shellenberger, M., Khara, H. S., Zhang, Y., Lee, M. T. M., Wolk, D. M., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm10010103 · Cited by 11 · see citing works

A Self-Supervised Framework for Laboratory Data Imputation in Electronic Health Records

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Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis

Beaulieu‐Jones, B. K., Lavage, D. R., Snyder, J. W., Moore, J. H., Pendergrass, S. A., & Bauer, C. R. (2018) · JMIR Medical Informatics · doi:10.2196/medinform.8960 · Cited by 169 · see citing works

LIFE: A Deep Learning Framework for Laboratory Data Imputation in Electronic Health RecordsPreprint

Heilbroner, S. P., Carter, C., Vidmar, D., Mueller, E. T., Stumpe, M. C., & Miotto, R. (2023) · medRxiv · doi:10.1101/2023.10.31.23297843

Imputation of Missing Data in Electronic Health Records Based on Patients’ Similarities

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