ArticleJournal of biomedical informatics2017
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Missing data handling in pediatric appendectomy research: current practices and National Surgical Quality Improvement Program (NSQIP) analysis.Surgical endoscopy · 2026Pooled it
- Handling missingness in electronic health record laboratory data for secondary use: a decision-making process.BMJ open · 2026Review
- Behavioral and Engagement Predictors of Arthroplasty Clinic No-Shows: A Calibrated Machine Learning Analysis.Arthroplasty today · 2026Article
- Design and Validation of an AI-Assisted Sequential Screening Framework for Psychological Distress in Glaucoma.medRxiv : the preprint server for health sciences · 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
- SMART: a new patient similarity estimation framework for enhanced predictive modeling in acute kidney injury.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data.JMIR medical informatics · 2025Article
- How AI Could Help Us in the Epidemiology and Diagnosis of Acute Respiratory Infections?Pathogens (Basel, Switzerland) · 2024Review
- Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.BMC infectious diseases · 2024Observational
- On operational definitions of mortality.Kidney research and clinical practice · 2024Article
- Associations between Outpatient Laboratory Test Age and Healthcare Utilization in Type 2 Diabetes Care.Journal of diabetes and metabolic disorders · 2023Article
- Article
- 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.BMC medical research methodology · 2023Review
- Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Increasing efficiency of SVMp+ for handling missing values in healthcare prediction.PLOS digital health · 2023Article
- Gender gaps in type 1 diabetes care.Acta diabetologica · 2023Article
- Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation.Journal of medical Internet research · 2023Article
- Combining data discretization and missing value imputation for incomplete medical datasets.PloS one · 2023Article
- lab: an R package for generating analysis-ready data from laboratory records.PeerJ. Computer science · 2023Article
- A Framework for Automating Psychiatric Distress Screening in Ophthalmology Clinics Using an EHR-Derived AI Algorithm.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for analyzing EHR data is limited and specific efficacy for postoperative complication detection is unclear. Several data imputation methods were used to develop data models for automated detection of three types (i.e., superficial, deep, and organ space) of surgical site infection (SSI) and overall SSI using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) Registry 30-day SSI occurrence data as a reference standard. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values. Missing data imputation appears to be an effective means for improving postoperative SSI detection using EHR clinical data.
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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.