Age density patterns in patients medical conditions: A clustering approach

Fahad Alhasoun; Faisal Aleissa; May Alhazzani; Luis G Moyano; Claudio Pinhanez; Marta C González

doi:10.1371/journal.pcbi.1006115

Age density patterns in patients medical conditions: A clustering approach

PLoS Comput Biol. 2018 Jun 26;14(6):e1006115. doi: 10.1371/journal.pcbi.1006115. eCollection 2018 Jun.

Authors

Fahad Alhasoun¹, Faisal Aleissa², May Alhazzani³, Luis G Moyano⁴, Claudio Pinhanez⁴, Marta C González^{1

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Affiliations

¹ Center for Computational Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
² Center for Complex Engineering Systems at KACST and MIT, Cambridge, Massachusetts, United States of America.
³ Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
⁴ IBM Research Labs, Rio de Janeiro, Brazil.
⁵ Department of City and Regional Planning, University of California, Berkeley, Berkeley, California, United States of America.
⁶ Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Age Factors*
Algorithms
Brazil
Cluster Analysis*
Disease
Epidemiologic Methods
Epidemiology / statistics & numerical data
Female
Humans
International Classification of Diseases
Male
Sex Factors*

Grants and funding

The research was supported in part by grants from the Center for Complex Engineering Systems at KACST and MIT and the MIT-Brazil MISTI program. We thank FGV-Rio and IBM Research Brazil for hosting MCG and F. Alhasoun during the design and initial stages of this work. All study procedures were carried out with Institutional Review Board approval from MIT COUHES (protocol #1405006399) approved on June 10, 2014. Data was collected by IBM for operational purposes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.