Age density patterns in patients medical conditions: A clustering approach
Name
journal.pcbi.1006115.pdf
Size
13.51 MB
Format
Adobe PDF
Checksum (MD5)
6dac6230e3727c04451b19c4e97c0036
Author(s)
Moyano, Luis G.
Alhasoun, Fahad
Aleissa, Faisal Saad
Alhazzani, May
Pinhanez, Claudio S.
Gonzalez, Marta C.
Date Issued
June 2018
Journal
PLOS Computational Biology
Publisher
Public Library of Science
Citation
Alhasoun, Fahad, Faisal Aleissa, May Alhazzani, Luis G. Moyano, Claudio Pinhanez, and Marta C. González. “Age Density Patterns in Patients Medical Conditions: A Clustering Approach.” Edited by Edwin Wang. PLOS Computational Biology 14, no. 6 (June 26, 2018): e1006115.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Computation for Design and Optimization Program
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1006115
