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

Jean Digitale1,2, Deborah Franzon3, Mark J Pletcher2

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|November 14, 2025
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Summary
This summary is machine-generated.

Handling missing data in electronic health records (EHR) is crucial for prediction models. Last Observation Carried Forward (LOCF) and native machine learning support offer efficient solutions for missing EHR data in clinical prediction.

Keywords:
clinical prediction modelselectronic health recordimputationmachine learningmissing data

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Area of Science:

  • Clinical Informatics
  • Biostatistics
  • Machine Learning

Background:

  • Missing data present a significant challenge in electronic health record (EHR)-based prediction modeling.
  • Traditional imputation methods may not be suitable for prediction or machine learning models, necessitating adaptable workflows for both development and real-time prediction.

Purpose of the Study:

  • To evaluate methods for handling missing data in EHRs for clinical prediction models in the pediatric intensive care unit (PICU).

Main Methods:

  • Generated synthetic datasets with varying missing data mechanisms and proportions from real EHR data.
  • Assessed imputation strategies: Last Observation Carried Forward (LOCF), random forest multiple imputation, and native support for missing values.
  • Evaluated performance for predicting successful extubation (binary) and blood pressure (continuous).

Main Results:

  • 18.2% of original EHR data were missing across 886 patients and 1220 intubation events.
  • LOCF demonstrated the lowest imputation error and generally outperformed other methods.
  • Imputation method performance varied more for binary outcomes than continuous outcomes.

Conclusions:

  • Traditional imputation methods may not be optimal for prediction models.
  • The proportion of missing data significantly impacted performance more than the missingness mechanism.
  • LOCF and native machine learning support provide efficient and effective solutions for handling missing EHR data in predictive analyses.