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Addressing Missingness in Predictive Models That Use Electronic Health Record Data.

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Area of Science:

  • Health Informatics
  • Clinical Epidemiology
  • Biostatistics

Background:

  • Electronic health record (EHR) data are crucial for developing clinical prediction models.
  • Missing data is a common issue in EHRs, impacting model accuracy and reliability.
  • Current guidelines for prediction models offer limited recommendations for handling missing EHR data.

Purpose of the Study:

  • To characterize missingness patterns in EHR data.
  • To summarize methods for addressing missing data in prediction model development.
  • To provide recommendations for validating and implementing prediction models with missing EHR data.

Main Methods:

  • Review of existing literature on missing data in EHRs.
  • Characterization of systematic and nonsystematic missingness in EHR datasets.
  • Summary of statistical techniques for handling missing data in prediction modeling.

Main Results:

  • EHR data exhibit both systematic and nonsystematic missingness.
  • Various imputation and modeling techniques can address missing data.
  • Lack of standardized guidelines for missing data in clinical prediction models.

Conclusions:

  • Addressing missing EHR data is essential for robust clinical prediction models.
  • Recommendations are provided for model development, validation, and implementation.
  • Further research is needed to improve handling of missing EHR data in clinical practice.