This research seminar explores disease associations following traumatic brain injury (TBI) through advanced data science methodologies. Using a comprehensive database of TBI patients, we employ network analysis to uncover patterns in disease progression (Mehta and Sudhakar, BMC Neurology 2025). By constructing network graphs that map co-occurring conditions, we identify structural properties that provide deep insights into how TBI patients may develop specific comorbidities. The findings from this study offer valuable implications for predictive healthcare, enabling early intervention strategies and personalized post-trauma care. By leveraging big data, statistical modeling and graph theory, we enhance our understanding of the complex relationships between TBI and subsequent health complications. As an interdisciplinary project, this research integrates data science, neurobiology, and mathematical modeling, with significant applications in psychiatry, health economics, and policy-making. Whether you are interested in health informatics, machine learning, or network science, there’s something here for everyone!

Meet Kaustav! With an undergraduate degree in Biological Sciences and a postgraduate diploma in Chemistry at Krea, Kaustav is always on the lookout for challenging, interdisciplinary research questions. Kaustav is currently a teaching fellow for the data science discipline at Krea. Passionate about both exploration and education, Kaustav loves sharing knowledge with enthusiasm—and yes, unlike batteries, jokes are always included!

The talk is mandatory for the students of DATA201

All are welcome!