Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation

Wen-wai Yim, Meliha Yetisgen, Jenny Huang, Micah Grossman

Abstract
For every patient’s visit to a clinician, a clinical note is generated documenting their medical conversation, including complaints discussed, treatments, and medical plans. Despite advances in natural language processing, automating clinical note generation from a clinic visit conversation is a largely unexplored area of research. Due to the idiosyncrasies of the task, traditional methods of corpus creation are not effective enough approaches for this problem. In this paper, we present an annotation methodology that is content- and technique- agnostic while associating note sentences to sets of dialogue sentences. The sets can further be grouped with higher order tags to mark sets with related information. This direct linkage from input to output decouples the annotation from specific language understanding or generation strategies. Here we provide data statistics and qualitative analysis describing the unique annotation challenges. Given enough annotated data, such a resource would support multiple modeling methods including information extraction with template language generation, information retrieval type language generation, or sequence to sequence modeling.
Anthology ID:
2020.lrec-1.52
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
413–421
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.52
DOI:
Bibkey:
Cite (ACL):
Wen-wai Yim, Meliha Yetisgen, Jenny Huang, and Micah Grossman. 2020. Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 413–421, Marseille, France. European Language Resources Association.
Cite (Informal):
Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation (Yim et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.52.pdf
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