The exponential growth of biomedical information far exceeds our cognitive abilities to exploit it for the prevention, diagnosis, and treatment of diseases. At the DIVA lab, we use visual analytics to discover and validate significant patterns in big and complex datasets, and translate those discoveries into computational innovations that enhance biomedical decision-making.


Awards: Outstanding Paper Award from SciTS 2018 Conference; Educator of the Month Award, UTMB

Grants: PCORI Award to build new big data visual analytical method; TCP pilot grant to build MODIM. Educational Innovation Grant from AMT, UTMB.

Publications: Team-Centered Informatics paper published in JABS. Hip-Fracture Phenotypes published in JMI, with news report.    

Principal Investigator

Suresh K. Bhavnani, Ph.D., M.Arch, FAMIA, Professor of Biomedical Informatics


Discover meaningful patterns in big biomedical data through visual analytics
Innovate approaches to amplify cognition and enhance decision making

Analysis of Renal Diseases and Genes in a 3D Immersive CAVE Analysis of Molecular Similarities and Differences between Renal Diseases Analysis of how Symptoms Overlap across Toxic Chemicals Design of a Decision-Support System for the Rapid Identification of Toxic Chemicals Contextual Analysis of MAIDN in a Fire Truck Analysis of Asthma Patients and Cytokines Analysis of Asthma Genotype and Phenotype Information [by Numan Oezguen] Analysis of how Interventions Co-Occur across Depression Clincal Trials Analysis of Uterine Magnetomyographic Sensor Activity in Pregnant Women Analysis of how Cancer Symptoms Co-Occur across Patients Analysis of how Cancer Symptom Severity Changes over Time Analysis of how Melanoma Facts are Scattered across Healthcare Webpages



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