Unsupervised machine learning applications in big public health data

LUIS G. MOYANO

Mendoza

Simposio; II simposio internacional de medicina traslacional; 2019

Facultades de Ciencias Médicas y de Ciencias Exactas y Naturales de la UNCuyo, junto a la Facultad de Farmacia y Bioquímica de la UBA y la Facultad de Medicina de la Albert-Ludwigs-Universität de Freiburg, Alemania

We will comment on 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.