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

Journal of the American Medical Informatics Association : JAMIA
Elyne ScheurwegsTim Van den Bulcke

Abstract

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. 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. 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 co...Continue Reading

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Citations

Jan 24, 2018·Journal of the American Medical Informatics Association : JAMIA·Honghan WuRichard J B Dobson
May 13, 2017·Clinical Journal of the American Society of Nephrology : CJASN·Anne-Sophie JannotNicolas Pallet
Jun 13, 2017·Journal of Biomedical Informatics·Elyne ScheurwegsKim Luyckx
May 26, 2021·Health Care Management Science·José Carlos FerrãoHenrique M G Martins
Mar 1, 2020·Health Systems·José Carlos FerrãoDaniel Gartner
Jul 27, 2021·Journal of Healthcare Engineering·Ayoub BagheriDaniel L Oberski
Apr 28, 2017·Mayo Clinic Proceedings. Innovations, Quality & Outcomes·Sudhi G UpadhyayaDaryl J Kor

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