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Subjective Data
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Temporal condition pattern mining in large, sparse electronic health record data: A case study in characterizing

Elizabeth A Campbell1,2, Ellen J Bass1,3, Aaron J Masino1,4

  • 1Department of Information Science, College of Computing & Informatics, Drexel University, Philadelphia, Pennsylvania, USA.

Journal of the American Medical Informatics Association : JAMIA
|February 13, 2020
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This study developed a new method to find patterns in electronic health records, revealing more diagnoses for pediatric asthma by mapping codes. This approach enhances understanding of disease progression and can be applied to other conditions.

Keywords:
asthmadata miningdata scienceelectronic health records

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Area of Science:

  • Health Informatics
  • Data Mining
  • Clinical Research

Background:

  • Electronic health records (EHRs) contain valuable data, but coded concepts are often sparse, hindering pattern discovery.
  • Understanding temporal condition patterns is crucial for diagnosing and managing diseases like pediatric asthma.
  • Existing methods may not fully capture the complexity of diagnostic trajectories due to data sparsity.

Purpose of the Study:

  • To introduce and validate a temporal condition pattern mining methodology for EHR data.
  • To address the challenge of sparse coded concept utilization in EHRs.
  • To uncover condition patterns associated with the initial diagnosis of pediatric asthma.

Main Methods:

  • Utilized the Sequential PAttern Discovery using Equivalence classes (SPADE) algorithm.
  • Applied SPADE to a large dataset (71,824 patients) from the Children's Hospital of Philadelphia.
  • Compared results from International Classification of Diseases (ICD) codes and mapped expanded diagnostic clusters (EDCs).

Main Results:

  • The mapped EDC dataset identified 36 unique diagnoses, compared to only 19 in the raw ICD dataset.
  • Temporal trends in condition diagnoses were discoverable using EDCs but not with raw ICD codes.
  • Mapping medical concepts into homogenous groups was essential for uncovering meaningful patterns.

Conclusions:

  • The developed methodology effectively mines temporal condition patterns from EHR data.
  • Mapping sparsely coded medical concepts is crucial for extracting valuable clinical insights.
  • This approach is applicable to studying diagnostic trajectories for various conditions and other coded medical concepts.