Age density patterns in patients medical conditions: A clustering approach

• Main Authors: Moyano, Luis G. (Author), Alhasoun, Fahad (Contributor), Aleissa, Faisal Saad (Contributor), Alhazzani, May (Contributor), Pinhanez, Claudio S. (Contributor), Gonzalez, Marta C. (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Institute for Data, Systems, and Society (Contributor), Massachusetts Institute of Technology. Computation for Design and Optimization Program (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor)
• Format: Article
• Language: English
• Published: Public Library of Science, 2018-11-05T14:56:25Z.
• Subjects: Article
• Online Access: Get fulltext

 This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.