Abstract: Electronic Medical Records (EMRs) have become the cornerstone of modern healthcare, capturing vast amounts of structured and unstructured patient data. Beyond lab results and diagnostic codes, textual data such as clinical notes, discharge summaries, and physician narratives hold rich insights that can drive personalized treatment strategies. Harnessing this information effectively is key to advancing precision medicine.
In this talk, Dr. Ramakanth Kavuluru from the University of Kentucky will explore how EMRs and textual data can be leveraged to enable more individualized and data-driven healthcare. The session will highlight computational approaches for extracting clinically relevant information, integrating diverse data sources, and applying machine learning techniques to support decision-making.
Key themes will include:
The role of textual data in complementing structured EMR fields for patient-specific insights.
Natural language processing (NLP) methods for mining clinical narratives.
Challenges of data quality, interoperability, and privacy in large-scale EMR analysis.
Applications of EMR-driven precision medicine in oncology, chronic disease management, and preventive care.
Future directions for combining EMRs with genomic and other multi-omics data.
The presentation will emphasize how computational advances can transform EMRs from passive repositories into active engines of precision medicine. By unlocking the potential of textual data, healthcare systems can move closer to delivering treatments tailored to the unique needs of each patient.