Abstract: Healthcare generates vast amounts of unstructured data every day, ranging from clinical notes and medical records to diagnostic reports and patient communications. Extracting meaningful information from this data is critical for improving patient care, enabling research, and supporting decision-making. Yet, the complexity of medical language, the diversity of data formats, and the need for accuracy and privacy present significant challenges.
In this talk, Dr. Raghava Muthuraju and Dr. Suman Roy from Oracle will explore the demands and challenges of information extraction in healthcare applications. The session will highlight how natural language processing (NLP), machine learning, and domain-specific ontologies can be leveraged to transform unstructured healthcare data into actionable insights.
Key themes will include:
The unique difficulties of processing medical text, including ambiguity, jargon, and context sensitivity.
Approaches to building robust information extraction pipelines for healthcare.
Balancing efficiency with accuracy in clinical applications.
Ensuring compliance with privacy regulations and ethical standards.
Opportunities for AI-driven solutions to support diagnostics, treatment planning, and healthcare analytics.
The presentation will emphasize both the technical hurdles and the transformative potential of information extraction in healthcare. By addressing these challenges, the speakers will show how AI and advanced computational methods can unlock new possibilities for scalable, reliable, and patient-centric healthcare solutions.