Ontology and Oncology: NLP for Precision Medicine

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This session gives an overview of the importance of precision medicine in cancer treatment and describes an approach used by UVA in the TREC 2018 Precision Medicine workshop. The PM track aims to encourage research into precision oncology medicine to provide more relevant information to physicians and researchers.

For this task we ranked articles from a corpus of bio-medical article abstracts from PubMed and MEDLINE for relevance for the treatment, prevention, and prognosis of the disease given specific medical information about each patient.

We demonstrated using a flexible graph-based query expansion method that existing medical ontologies can be leveraged to improve precision in document relevance ranking with little to no other clinical input.

Sean Mullane

University of Virginia

Sean is currently working as a data scientist in the UVA Health System while completing a Master's degree in data science at the UVA Data Science Institute where he is researching the applications of machine learning to protein structure prediction. He has lived in Charlottesville since graduating from UVA as an undergraduate. Current areas of work include deep learning, natural language processing and precision medicine.