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Inferring breast cancer concomitant diagnosis and comorbidities from the Nationwide Inpatient Sample using social network analysis

Author

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  • Radhakrishnan Nagarajan
  • Shengfan Zhang
  • Fay Cobb Payton
  • Suleiman Massarweh

Abstract

Breast cancer is a complex disease and may be accompanied by other multiple health conditions. The present study investigates associations between diagnosis codes in breast cancer patients using the Nationwide Inpatient Sample data. Concomitant diagnoses codes are identified by statistically significant associations between the diagnoses codes in a given breast cancer patient. These are subsequently represented in the form of a network (Breast Cancer Concomitant Diagnosis Network (BCCDN)). In contrast to more classical approaches, BCCDN provides system-level insights and convenient visualization reflected by the complex wiring patterns between the diagnoses codes. Social network analysis is used to investigate highly connected codes in the BCCDN network, and their variation across three different populations: (i) the deceased breast cancer population (ii) the elderly breast cancer population (age>65 years) and (iii) the adult breast cancer population (age

Suggested Citation

  • Radhakrishnan Nagarajan & Shengfan Zhang & Fay Cobb Payton & Suleiman Massarweh, 2014. "Inferring breast cancer concomitant diagnosis and comorbidities from the Nationwide Inpatient Sample using social network analysis," Health Systems, Taylor & Francis Journals, vol. 3(2), pages 136-142, June.
    • Handle: RePEc:taf:thssxx:v:3:y:2014:i:2:p:136-142
    DOI: 10.1057/hs.2014.4
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