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Deriving comorbidities from medical records using natural language processing.

Hojjat Salmasian1, Daniel E Freedberg, Carol Friedman

  • 1Department of Biomedical Informatics, Columbia University, New York, USA.

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|November 2, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for extracting comorbidity data from electronic medical records, achieving high accuracy. This approach can aid in predicting patient outcomes like mortality and readmission.

Keywords:
ComorbidityConfounding FactorsNatural Language Processing

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Area of Science:

  • Medical Informatics
  • Clinical Research
  • Natural Language Processing

Background:

  • Comorbidity information is vital for phenotypic studies due to its confounding effects.
  • Accurate comorbidity extraction from electronic medical records (EMRs) is challenging but essential.

Purpose of the Study:

  • To develop and validate an automated method for extracting comorbidity information from EMRs.
  • To compare the performance of automated extraction against traditional claims data.

Main Methods:

  • A modified Charlson Comorbidity Index (CCI) was used, with a reference standard created by two physicians from 100 admission notes.
  • The MedLEE natural language processing system processed notes, and queries were written for automated comorbidity extraction.
  • Interrater agreement for the reference set was 97.7%.

Main Results:

  • The automated method achieved an F1 score of 0.761, with no significant difference in summed CCI scores compared to the reference standard.
  • Automated extraction outperformed claims data (F1 score 0.741) due to higher sensitivity (66.1%).

Conclusions:

  • The developed automated method accurately determines comorbidities from EMRs.
  • This technique enables automated prediction of mortality and readmission, leveraging the validated predictive power of the CCI.