Age density patterns in patients medical conditions: A clustering approach
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 vi...
| Main Authors: | , , , , , |
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| Format: | Article |
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Public Library of Science
2018
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| Online Access: | http://hdl.handle.net/1721.1/118874 https://orcid.org/0000-0001-6846-9858 https://orcid.org/0000-0001-9053-8186 https://orcid.org/0000-0002-0496-7206 https://orcid.org/0000-0002-8482-0318 |
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| author | Moyano, Luis G. Alhasoun, Fahad Aleissa, Faisal Saad Alhazzani, May Pinhanez, Claudio S. Gonzalez, Marta C. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Moyano, Luis G. Alhasoun, Fahad Aleissa, Faisal Saad Alhazzani, May Pinhanez, Claudio S. Gonzalez, Marta C. |
| author_sort | Moyano, Luis G. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:09:05Z |
| format | Article |
| id | mit-1721.1/118874 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:09:05Z |
| publishDate | 2018 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/1188742022-09-30T07:55:34Z Age density patterns in patients medical conditions: A clustering approach Moyano, Luis G. Alhasoun, Fahad Aleissa, Faisal Saad Alhazzani, May Pinhanez, Claudio S. Gonzalez, Marta C. 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) Alhasoun, Fahad Aleissa, Faisal Saad Alhazzani, May Pinhanez, Claudio S. Gonzalez, Marta C. 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. 2018-11-05T14:56:25Z 2018-11-05T14:56:25Z 2018-06 2017-02 2018-10-12T16:06:46Z Article http://purl.org/eprint/type/JournalArticle 1553-7358 http://hdl.handle.net/1721.1/118874 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. https://orcid.org/0000-0001-6846-9858 https://orcid.org/0000-0001-9053-8186 https://orcid.org/0000-0002-0496-7206 https://orcid.org/0000-0002-8482-0318 http://dx.doi.org/10.1371/journal.pcbi.1006115 PLOS Computational Biology Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science PLoS |
| spellingShingle | Moyano, Luis G. Alhasoun, Fahad Aleissa, Faisal Saad Alhazzani, May Pinhanez, Claudio S. Gonzalez, Marta C. Age density patterns in patients medical conditions: A clustering approach |
| title | Age density patterns in patients medical conditions: A clustering approach |
| title_full | Age density patterns in patients medical conditions: A clustering approach |
| title_fullStr | Age density patterns in patients medical conditions: A clustering approach |
| title_full_unstemmed | Age density patterns in patients medical conditions: A clustering approach |
| title_short | Age density patterns in patients medical conditions: A clustering approach |
| title_sort | age density patterns in patients medical conditions a clustering approach |
| url | http://hdl.handle.net/1721.1/118874 https://orcid.org/0000-0001-6846-9858 https://orcid.org/0000-0001-9053-8186 https://orcid.org/0000-0002-0496-7206 https://orcid.org/0000-0002-8482-0318 |
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