Automated Orthodontic Diagnosis from a Summary of Medical Findings
Takumi Ohtsuka, Tomoyuki Kajiwara, Chihiro Tanikawa, Yuujin Shimizu, Hajime Nagahara, Takashi Ninomiya
Abstract
We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.- Anthology ID:
- 2023.clinicalnlp-1.21
- Volume:
- Proceedings of the 5th Clinical Natural Language Processing Workshop
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Anna Rumshisky
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 156–160
- Language:
- URL:
- https://aclanthology.org/2023.clinicalnlp-1.21
- DOI:
- 10.18653/v1/2023.clinicalnlp-1.21
- Cite (ACL):
- Takumi Ohtsuka, Tomoyuki Kajiwara, Chihiro Tanikawa, Yuujin Shimizu, Hajime Nagahara, and Takashi Ninomiya. 2023. Automated Orthodontic Diagnosis from a Summary of Medical Findings. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 156–160, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Automated Orthodontic Diagnosis from a Summary of Medical Findings (Ohtsuka et al., ClinicalNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.clinicalnlp-1.21.pdf