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Start Over Author "Alhazzani, May" Topic age factors
2 results on '"Alhazzani, May"'

1. Age density patterns in patients medical conditions: A clustering approach.

Author

Alhasoun F, Aleissa F, Alhazzani M, Moyano LG, Pinhanez C, and González MC

Subjects
Algorithms, Brazil, Disease, Epidemiologic Methods, Epidemiology statistics & numerical data, Female, Humans, International Classification of Diseases, Male, Age Factors, Cluster Analysis, Sex Factors
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., Competing Interests: The authors have declared that no competing interests exist.

Published
2018
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2. Age density patterns in patients medical conditions: A clustering approach

Author

Fahad Alhasoun, Marta C. González, Luis Gregorio Moyano, Faisal Aleissa, Claudio S. Pinhanez, May Alhazzani, 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. Program in Computation for Design and Optimization, Program in Media Arts and Sciences (Massachusetts Institute of Technology), Alhasoun, Fahad, Aleissa, Faisal Saad, Alhazzani, May, Pinhanez, Claudio S., and Gonzalez, Marta C.

Subjects
0301 basic medicine, Male, Viral Diseases, Medical Doctors, Epidemiology, Health Care Providers, purl.org/becyt/ford/1.7 [https], Health records, Geographical locations, purl.org/becyt/ford/1 [https], 0302 clinical medicine, Elderly, Chickenpox, Medicine and Health Sciences, Cluster Analysis, Disease, Medical Personnel, lcsh:QH301-705.5, COMPLEX NETWORKS, Geriatrics, Heterogeneous sample, Ecology, Age Factors, Professions, Geography, Infectious Diseases, Computational Theory and Mathematics, Modeling and Simulation, Female, HEALTHCARE, Algorithms, Brazil, Research Article, medicine.medical_specialty, Large population, Age and sex, 03 medical and health sciences, Cellular and Molecular Neuroscience, DATA ANALYSIS, Sex Factors, Age Distribution, Population Metrics, International Classification of Diseases, Diagnostic Medicine, Genetics, medicine, Humans, In patient, Cluster analysis, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Population Density, Population Biology, Biology and Life Sciences, South America, medicine.disease, Comorbidity, Health Care, 030104 developmental biology, lcsh:Biology (General), Age Groups, Population Groupings, People and places, COMORBIDITY, Epidemiologic Methods, 030217 neurology & neurosurgery, Demography
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., Author summary Age and sex of a patient can be directly related to susceptibilities to certain medical conditions. We present a method to generate clusters of human phenotype, based on the age of the population. This method helps extract knowledge on age and sex from the data. The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions.

Published
2017

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