Summary of the paper
| Title | SemScribe: Natural Language Generation for Medical Reports |
|---|---|
| Authors | Sebastian Varges, Heike Bieler, Manfred Stede, Lukas C. Faulstich, Kristin Irsig and Malik Atalla |
| Abstract | Natural language generation in the medical domain is heavily influenced by domain knowledge and genre-specific text characteristics. We present SemScribe, an implemented natural language generation system that produces doctor's letters, in particular descriptions of cardiological findings. Texts in this domain are characterized by a high density of information and a relatively telegraphic style. Domain knowledge is encoded in a medical ontology of about 80,000 concepts. The ontology is used in particular for concept generalizations during referring expression generation. Architecturally, the system is a generation pipeline that uses a corpus-informed syntactic frame approach for realizing sentences appropriate to the domain. The system reads XML documents conforming to the HL7 Clinical Document Architecture (CDA) Standard and enhances them with generated text and references to the used data elements. We conducted a first clinical trial evaluation with medical staff and report on the findings. |
| Topics | Natural Language Generation, Ontologies, Other |
Full paper  |
SemScribe: Natural Language Generation for Medical Reports |
| Bibtex | @InProceedings{VARGES12.165, author = {Sebastian Varges and Heike Bieler and Manfred Stede and Lukas C. Faulstich and Kristin Irsig and Malik Atalla}, title = {SemScribe: Natural Language Generation for Medical Reports}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } |