Unsupervised Machine Learning for Co/Multimorbidity Analysis


  •  Shatrunjai P. Singh    
  •  Swagata Karkare    
  •  Sudhir M. Baswan    
  •  Vijendra P. Singh    

Abstract

Although co/multimorbidities are associated with a significant increase in mortality, lack of quantitative exploratory techniques often impedes an in-depth analysis of their association. In the current study, we explore the clustering of co/multimorbid patients in the Texas patient population. We employ unsupervised agglomerative hierarchical clustering to find clusters of co/multimorbid patients within this population. Our analysis revealed the presence of nine distinct, clinically relevant clusters of co/multimorbidities within the study population of interest. This technique provides a quantitative exploratory analysis of the co/multimorbidities present in a specific population.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1927-7032
  • Issn(Onlne): 1927-7040
  • Started: 2012
  • Frequency: bimonthly

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