Reimer AP, Dai W, Smith B, Schiltz NK, Sun J, Koroukian SM. Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.
Int J Med Inform 2021;
156:104588. [PMID:
34607290 DOI:
10.1016/j.ijmedinf.2021.104588]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/17/2021] [Accepted: 09/19/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND
Electronic health record (EHR) data is commonly used for secondary purposes such as research and clinical decision support. However, reuse of EHR data presents several challenges including but not limited to identifying all diagnoses associated with a patient's clinical encounter. The purpose of this study was to assess the feasibility of developing a schema to identify and subclassify all structured diagnosis codes for a patient encounter.
METHODS
To develop a subclassification schema we used EHR data from an interhospital transport data repository that contained complete hospital encounter level data. Eight discrete data sources containing structured diagnosis codes were identified. Diagnosis codes were normalized using the Unified Medical Language System and additional EHR data were combined with standardized terminologies to create and validate the subcategories. We then employed random forest to assess the usefulness of the new subcategorized diagnoses to predict post-interhospital transfer mortality by building 2 models, one using standard diagnosis codes, and one using the new subcategorized diagnosis codes.
RESULTS
Six subcategories of diagnoses were identified and validated. The subcategories included: primary or admitting diagnoses (10%), past medical, surgical or social history (9%), problem list (20%), comorbidity (24%), discharge diagnoses (6%), and unmapped diagnoses (31%). The subcategorized model outperformed the standard model, achieving a training AUROC of 0.97 versus 0.95 and testing model AUROC of 0.81 versus 0.46.
DISCUSSION
Our work demonstrates that merging structured diagnosis codes with additional EHR data and secondary data sources provides additional information to understand the role of diagnosis throughout a clinical encounter and improves predictive model performance. Further work is necessary to assess if subcategorizing produces benefits in interpreting the results of prognostic models and/or operationalizing the results in clinical decision support applications.
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