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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis

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This study reveals significant disease connections, particularly between circulatory and metabolic conditions. Understanding these comorbidities aids in early risk identification and improved prevention strategies.

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Area of Science:

  • Medical Informatics
  • Network Medicine
  • Public Health

Background:

  • Disease co-occurrence, or comorbidity, is increasingly recognized but complex network associations remain underexplored.
  • Understanding how different diseases cluster within individuals is crucial for comprehensive healthcare.
  • Existing research has not fully acknowledged the intricate, short-term and long-term connections between various medical conditions.

Purpose of the Study:

  • To investigate the network association patterns among co-occurring diseases within the same individual.
  • To apply association rule mining techniques to uncover hidden relationships between diagnoses.
  • To identify significant disease combinations for improved clinical insights.

Main Methods:

  • Utilized a large-scale electronic medical record database from Xuzhou Medical University (2015-2020).
  • Analyzed 1,551,732 diagnoses from 144,207 patients, categorized using the International Classification of Diseases, 10th Revision (ICD-10).
  • Employed the Apriori algorithm, a data mining technique, to discover association rules among diagnoses.

Main Results:

  • Generated 12,889 initial association rules, refined to 110 significant disease combinations after filtering (support ≥ 0.001, confidence ≥ 60%, lift > 1).
  • Identified strong associations, with a notable proportion involving circulatory system diseases and metabolic diseases.
  • Highlighted the prevalence of specific disease clusters within the patient cohort.

Conclusions:

  • Successfully elucidated complex network associations between disorders across different body systems in individuals.
  • Demonstrated the efficacy of the Apriori algorithm for analyzing comorbidity and multimorbidity data.
  • The identified disease combinations offer valuable insights for enhancing prevention strategies, early detection of high-risk populations, and reducing mortality.