|
Title
|
|
|
|
Data integration of structured and unstructured sources for assigning clinical codes to patient stays
| |
|
Author
|
|
|
|
| |
|
Abstract
|
|
|
|
| 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. |
| |
|
Language
|
|
|
|
English
| |
|
Source (journal)
|
|
|
|
Journal of the American Medical Informatics Association. - Philadelphia, Pa
| |
|
Publication
|
|
|
|
Philadelphia, Pa
:
2016
| |
|
ISSN
|
|
|
|
1067-5027
| |
|
DOI
|
|
|
|
10.1093/JAMIA/OCV115
| |
|
Volume/pages
|
|
|
|
23
(2016)
, p. 11-19
| |
|
ISI
|
|
|
|
000375292600003
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|
Full text (open access)
|
|
|
|
| |
|