ArticleBMC research notes2022
A multi-step approach to managing missing data in time and patient variant electronic health records.
Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Benchmarking imputation methods on real-world clinical time series with simulated spatio-temporal missingness.Nature communications · 2026Article
- Quality of Life and Treatment Satisfaction in People with HIV Switching to Bictegravir/Emtricitabine/Tenofovir Alafenamide: Pooled Analysis from Observational Cohort Studies.Infectious diseases and therapy · 2026Article
- Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare.Healthcare (Basel, Switzerland) · 2025Article
- Decentralized Clinical Trials in the Era of Real-World Evidence: A Statistical Perspective.Clinical and translational science · 2025Review
- Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records.BMC medical informatics and decision making · 2024Article
- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
- A Web-Based Model to Predict a Neurological Disorder Using ANN.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectiveElectronic health records (EHR) hold promise for conducting large-scale analyses linking individual characteristics to health outcomes. However, these data often contain a large number of missing values at both the patient and visit level due to variation in data collection across facilities, providers, and clinical need. This study proposes a stepwise framework for imputing missing values within a visit-level EHR dataset that combines informative missingness and conditional imputation in a scalable manner that may be parallelized for efficiency.
resultsFor this study we use a subset of data from AMPATH representing information from 530,812 clinic visits from 16,316 Human Immunodeficiency Virus (HIV) positive women across Western Kenya who have given birth. We apply this process to a set of 84 clinical, social and economic variables and are able to impute values for 84.6% of variables with missing data with an average reduction in missing data of approximately 35.6%. We validate the use of this imputed dataset by predicting National Hospital Insurance Fund (NHIF) enrollment with 94.8% accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.