ArticleJournal of the American Medical Informatics Association : JAMIA2016
Data integration of structured and unstructured sources for assigning clinical codes to patient stays.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- Domain and Language adaptive pre-training of BERT models for Korean-English bilingual clinical text analysis.BMC medical informatics and decision making · 2025Article
- Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.Sensors (Basel, Switzerland) · 2025Article
- Effective Structured Information Extraction from Chest Radiography Reports Using Open-Weights Large Language Models.Radiology · 2025Article
- Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.JAMIA open · 2024Article
- Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.JAMIA open · 2024Review
- Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data.Journal of affective disorders · 2024Article
- Migraine headache (MH) classification using machine learning methods with data augmentation.Scientific reports · 2024Article
- Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach.Journal of personalized medicine · 2023Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- The Use and Structure of Emergency Nurses' Triage Narrative Data: Scoping Review.JMIR nursing · 2023Article
- Emergency nurses' triage narrative data, their uses and structure: a scoping review protocol.BMJ open · 2022Article
- Comparison of different feature extraction methods for applicable automated ICD coding.BMC medical informatics and decision making · 2022Article
- The prediction of hospital length of stay using unstructured data.BMC medical informatics and decision making · 2021Observational
- Leveraging electronic health record data to inform hospital resource management : A systematic data mining approach.Health care management science · 2021Article
- Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports.Journal of healthcare engineering · 2021Article
- Can structured EHR data support clinical coding? A data mining approach.Health systems (Basingstoke, England) · 2020Article
- Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data.Applied clinical informatics · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEnormous 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.
methodsTwo 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.
resultsWhen 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.
conclusionsWe 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.
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