ArticleJournal of biomedical informatics2023
Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
- MANNERS: A strategy for representation learning in multivariate datasets with high proportions of missing data.Patterns (New York, N.Y.) · 2026Article
- Using Electronic Health Records to Enhance Lyme Disease Surveillance: Protocol for the SubLyme Network.JMIR research protocols · 2026Article
- Imputation Free Deep Survival Prediction with Conditional Variational Autoencoders.Journal of healthcare informatics research · 2026Article
- Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection.BMC medical research methodology · 2026Article
- Are we reporting well enough? A systematic survey of missing data in patient-reported outcomes from non-small cell lung cancer randomized trials.Health and quality of life outcomes · 2026Article
- Early Prediction of Adverse Stroke Outcomes Using Nonclinical Factors and Missing Data: A Machine Learning Study.Cerebrovascular diseases (Basel, Switzerland) · 2026Article
- Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care.Frontiers in medicine · 2026Article
- A novel lightweight deep learning model for early prediction of cardiovascular disease.Frontiers in artificial intelligence · 2026Article
- Machine learning-based prediction algorithm of spontaneous preterm birth using multi-source data.BMC pregnancy and childbirth · 2025Article
- Clustering-Informed Shared-Structure Variational Autoencoder for Missing Data Imputation in Large-Scale Healthcare Data.Statistics in medicine · 2025Article
- Reducing Information and Selection Bias in EHR-Linked Biobanks via Genetics-Informed Multiple Imputation and Sample Weighting.medRxiv : the preprint server for health sciences · 2024Article
- Analyzing missingness patterns in real-world data using the SMDI toolkit: application to a linked EHR-claims pharmacoepidemiology study.BMC medical research methodology · 2024Article
- Filling the gaps: leveraging large language models for temporal harmonization of clinical text across multiple medical visits for clinical prediction.medRxiv : the preprint server for health sciences · 2024Article
- Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.medRxiv : the preprint server for health sciences · 2023Article
- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
- Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Real-world data mining meets clinical practice: Research challenges and perspective.Frontiers in big data · 2022Article
Corrections and comments
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Authors and funding
40 authors.
Funding
Abstract
backgroundIn electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients.
methodsWe collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern.
resultsWith these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors.
conclusionIn this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.
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