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Discovering sequential patterns and interrelations among multiple diseases in electronic medical records using cSPADE

He Ma1,2, Qianxin Huang3, Hong Zhang4

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Summary
This summary is machine-generated.

This study reveals significant sequential disease patterns and time intervals between diagnoses, offering insights into comorbidity. Findings highlight gender-specific disease progressions, aiding in clinical decision support.

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ComorbiditySequential pattern miningcSPADE

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Area of Science:

  • Computational epidemiology
  • Health informatics
  • Biostatistics

Background:

  • Understanding disease onset sequences is crucial for comorbidity research and predicting patient outcomes.
  • Temporal disease relationships inform disease progression and intervention strategies.

Purpose of the Study:

  • To investigate interdependencies and chronological disease order using sequential pattern mining.
  • To analyze time intervals between distinct disorder onsets.
  • To examine gender-based differences in disease sequence patterns.

Main Methods:

  • Utilized electronic medical record data from 269,973 patients (2012-2022).
  • Employed the Sequential Pattern Discovery using Equivalence Classes (SPADE) algorithm.
  • Analyzed 1,060,344 diagnostic entries with International Classification of Diseases, Tenth Revision (ICD-10) codes.

Main Results:

  • Identified 212 significant sequential comorbidity patterns, primarily involving endocrine and circulatory systems.
  • Disease onset intervals varied from under 2 months to 5-10 years, with many between 1-2 years.
  • 176 patterns showed stronger support in males; cardiovascular/liver diseases were more common in males, orthopedic/endocrine in females.

Conclusions:

  • The constrained SPADE (cSPADE) algorithm is effective for uncovering clinically relevant sequential comorbidity patterns.
  • Identified patterns can advance disease prevention, etiological research, and clinical decision support systems.