We are in the process of developing a new NLU system for biomedical texts. More will be added to this page as it is developed. For the present, see the readings below for some preliminary work which will provide some of the underpinnings for this new system.
- Schlegel, D.R., Bona, J., and Elkin, P.L., Comparing Small Graph Retrieval Performance for Ontology Concepts in Medical Texts (Invited Paper). In F. Wang, G. Luo, C. Luo, A. Khan, P. Mitra, and C. Yu, Eds., Proceddings of the First International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH) and Big-O(Q): Big Graphs Online Querying, Lecture Notes in Computer Science, Springer-Verlag, Berlin, 2015 (in-press).
- Schlegel, D.R., Crowner, C., and Elkin, P.L., Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia. MEDINFO 2015, August 2015, 619–623.
- Schlegel, D.R., Kaushik, S., and Elkin, P.L., Clinical Relevance of the Doctors Dilemma Question Set (Poster Abstract). AMIA Annual Symposium Proceedings 2015, American Medical Informatics Association, 2015. [Poster]
- Schlegel, D.R., Crowner, C., LeHoullier, F., and Elkin, P.L., HTP-NLP: A New NLP System for High Throughput Phenotyping. Informatics for Health 2017, April 2017.
- Schlegel, D.R., An Architecture for Automatic Generation of Computer Interpretable Guidelines. AMIA NLP-WG Pre-Symposium 2017, November 2017. [Poster]