Comorbidity Patterns and Its Impact on Health Outcomes: Two-Way Clustering Analysis
| Autor: | Petros Levounis, Debopriya Ghosh, Nabil R. Adam, Tarek N. Adam, Javier Cabrera |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Information Systems and Management Computer science Population Big data Sample (statistics) Logistic regression computer.software_genre 01 natural sciences 010104 statistics & probability 03 medical and health sciences Health care medicine 0101 mathematics Cluster analysis education education.field_of_study business.industry Regression analysis medicine.disease Comorbidity 030104 developmental biology Data mining business computer Information Systems |
| Zdroj: | IEEE Transactions on Big Data. 6:359-368 |
| ISSN: | 2372-2096 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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