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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Related Experiment Video

Updated: Oct 18, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.

Andrew P Reimer1, Wei Dai2, Benjamin Smith3

  • 1Frances Payne Bolton School of Nursing, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, United States; Critical Care Transport, Cleveland Clinic, 9800 Euclid Ave, Cleveland, OH, United States.

International Journal of Medical Informatics
|October 4, 2021
PubMed
Summary

Developing a new schema to subclassify electronic health record (EHR) diagnoses improved predictive model performance. This approach enhances understanding of diagnostic roles in clinical encounters for better EHR data utilization.

Keywords:
Data managementElectronic data processingElectronic health recordsMachine learning

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Area of Science:

  • Health Informatics
  • Clinical Data Analysis
  • Predictive Modeling

Background:

  • Electronic health record (EHR) data is vital for research and clinical decision support.
  • Identifying all diagnoses within a patient encounter from EHR data is a significant challenge.
  • Current methods often fail to capture the full diagnostic picture during a clinical encounter.

Purpose of the Study:

  • To assess the feasibility of a novel schema for identifying and subcategorizing all structured diagnosis codes within a patient encounter.
  • To improve the granularity and utility of diagnosis data extracted from EHRs.
  • To enhance secondary use of EHR data for research and clinical applications.

Main Methods:

  • Utilized EHR data from an interhospital transport repository with complete encounter-level data.
  • Normalized diagnosis codes using the Unified Medical Language System (UMLS) and integrated additional EHR data.
  • Developed and validated six diagnosis subcategories (e.g., primary, history, comorbidity, discharge).
  • Employed random forest models to compare predictive performance of standard vs. subcategorized diagnoses for post-transfer mortality.

Main Results:

  • Successfully identified and validated six distinct diagnosis subcategories.
  • The subcategorized diagnosis model significantly outperformed the standard model in predicting mortality (testing AUROC 0.81 vs. 0.46).
  • Subcategories represented varying proportions of diagnoses, including primary (10%), history (9%), problem list (20%), comorbidity (24%), discharge (6%), and unmapped (31%).

Conclusions:

  • Merging structured diagnosis codes with additional EHR and secondary data sources enhances understanding of diagnostic roles.
  • Subcategorization of diagnoses improves predictive model performance, demonstrating the value of granular diagnostic information.
  • Further research is needed to explore benefits in prognostic model interpretation and clinical decision support.