Data-driven visual characterization of patient health-status using electronic health records and self-organizing maps

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Chushig-Muzo, David, Soguero-Ruiz, Cristina, Engelbrecht, Andries, de Miguel Bohoyo, Pablo, Mora-Jimenez, Inmaculada
• Format: Article
• Language: English
• Subjects: Chronic Conditions; Chronic illnesses; Clustering; Clustering methods; Data visualization; Diabetes; Diseases; Drugs; Electronic health records; Hypertension; Insulin; Machine learning; Methodology; Patients; Pregnancy; Prototypes; Public health; Self organizing maps; Self-organizing feature maps; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3012082

Hypertension and diabetes have become a global health and economic issue, being among the major chronic conditions worldwide, particularly in developed countries. To face this global problem, a better knowledge about these diseases becomes crucial to characterize chronic patients. Our aim is two-fold: (1) to provide an efficient visual tool for identifying clinical patterns in high-dimensional data; and (2) to characterize the patient health-status through a data-driven approach using electronic health records of healthy, hypertensive and diabetic populations. We propose a two-stage methodology that uses diagnosis and drug codes of healthy and chronic patients associated to the University Hospital of Fuenlabrada in Spain. The first stage applies the Self-Organizing Map on the aforementioned data to get a set of prototype patients which are projected onto a grid of nodes. Each node has associated a prototype patient that captures relationships among clinical characteristics. In the second stage, clustering methods are applied on the prototype patients to find groups of patients with a similar health-status. Clusters with distinctive patterns linked to chronic conditions were found, being the most remarkable highlights: a cluster of pregnant women emerged among the hypertensive population, and two clusters of diabetic individuals with significant differences in drug-therapy (insulin and non-insulin dependant). The proposed methodology showed to be effective to explore relationships within clinical data and to find patterns related to diabetes and hypertension in a visual way. Our methodology raises as a suitable alternative for building appropriate clinical groups, becoming a promising approach to be applied to any population due to its data-driven philosophy. A thorough analysis of these groups could spawn new and fruitful findings.