Comorbidity Patterns and Its Impact on Health Outcomes: Two-Way Clustering Analysis

Debopriya Ghosh, Javier Cabrera, Tarek N. Adam, Petros Levounis

Research output: Contribution to journalArticle

Abstract

Comorbidity greatly increases the complexity of managing disease in patients. Approximately 27 percent of the US population have two or more concurrent comorbid conditions. Traditional models for assessing the impact of patient demographic and comorbidity burden on patient health outcomes, represented comorbidity conditions by Charlson Comorbidity Index. In this paper, we develop a novel two-way clustering approach combining model-based and weighted K-means clustering methods for characterizing and summarizing a patient's comorbid conditions. Our two-way approach helps reduce the size of the data to a manageable size, thus being practical for big data applications. Another novel aspect of our approach is the ability to handle weighted observations. Assigning weights to observations helps reduce the size of the dataset, thus addressing the scalability challenge of algorithms when dealing with big data. Using the National Inpatient Sample database for 2008-2013, we evaluate the performance of our approach by the use of logistic regression and support vector machine models by applying them to patients whose primary diagnosis is cardiovascular disease. In addition to evaluating our proposed method using empirical test data, we use asymptotic statistics. Both evaluation methods show that the proposed approach improves the prediction of patient health outcomes; specifically, hospital length of stay.

Original languageEnglish (US)
Article number7727984
Pages (from-to)359-368
Number of pages10
JournalIEEE Transactions on Big Data
Volume6
Issue number2
DOIs
StatePublished - Jun 1 2020

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Information Systems

Keywords

  • cardiovascular disease
  • Comorbidity conditions
  • model-based clustering
  • two-way clustering
  • weighted k-means clustering
  • weighted observations

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