Publication
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
Volume/pages
23(2016), p. 11-19
ISI
000375292600003
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Data fusion and structured input and output Machine Learning techniques for automated clinical coding.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 05.01.2016
Last edited 26.11.2017
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