Data integration of structured and unstructured sources for assigning clinical codes to patient stays

J Am Med Inform Assoc. 2016 Apr;23(e1):e11-9. doi: 10.1093/jamia/ocv115. Epub 2015 Aug 27.


Objective: Enormous amounts of healthcare data are becoming increasingly accessible through the large-scale adoption of electronic health records. In this work, structured and unstructured (textual) data are combined to assign clinical diagnostic and procedural codes (specifically ICD-9-CM) to patient stays. We investigate whether integrating these heterogeneous data types improves prediction strength compared to using the data types in isolation.

Methods: Two separate data integration approaches were evaluated. Early data integration combines features of several sources within a single model, and late data integration learns a separate model per data source and combines these predictions with a meta-learner. This is evaluated on data sources and clinical codes from a broad set of medical specialties.

Results: When compared with the best individual prediction source, late data integration leads to improvements in predictive power (eg, overall F-measure increased from 30.6% to 38.3% for International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes), while early data integration is less consistent. The predictive strength strongly differs between medical specialties, both for ICD-9-CM diagnostic and procedural codes.

Discussion: Structured data provides complementary information to unstructured data (and vice versa) for predicting ICD-9-CM codes. This can be captured most effectively by the proposed late data integration approach.

Conclusions: We demonstrated that models using multiple electronic health record data sources systematically outperform models using data sources in isolation in the task of predicting ICD-9-CM codes over a broad range of medical specialties.

Keywords: clinical coding; data integration; data mining; electronic health records; international classification of diseases.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clinical Coding / methods*
  • Data Mining
  • Datasets as Topic
  • Electronic Health Records / organization & administration*
  • Humans
  • International Classification of Diseases*
  • Machine Learning