@inproceedings{ohtsuka-etal-2023-automated,
    title = "Automated Orthodontic Diagnosis from a Summary of Medical Findings",
    author = "Ohtsuka, Takumi  and
      Kajiwara, Tomoyuki  and
      Tanikawa, Chihiro  and
      Shimizu, Yuujin  and
      Nagahara, Hajime  and
      Ninomiya, Takashi",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.clinicalnlp-1.21",
    doi = "10.18653/v1/2023.clinicalnlp-1.21",
    pages = "156--160",
    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.",
}

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.
Ohtsuka, Takumi Kajiwara, Tomoyuki Tanikawa, Chihiro Shimizu, Yuujin Nagahara, Hajime Ninomiya, Takashi
Automated Orthodontic Diagnosis from a Summary of Medical Findings