This talk will explore the integration of genomics with electronic health records (EHRs) in biobanks, emphasizing its potential to enhance women's health research. We'll delve into how EHR-linked biobanks can be leveraged to uncover genetic associations with conditions predominantly affecting women, such as endometriosis and discuss strategies for overcoming the challenges in this interdisciplinary field. By bridging these domains, we aim to pave the way for precision medicine approaches tailored specifically to women's health needs.
Learning Objectives:
1. Discuss the integration of EHR data with genomic research to enhance studies on women's health.
2. Evaluate the application of machine learning clustering techniques to identify endometriosis subtypes and their implications for risk prediction.
3. Examine the potential of machine learning-driven diagnostics to reduce the time to diagnosis and improve early intervention strategies in endometriosis.