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Research Publications

Department of Transformative System for Medical Information, Graduate School of Medicine, The University of Osaka

Journal Papers

2025

  1. Sugimoto K, Wada S, Konishi S, Sato J, Okada K, Kido S, Tomiyama N, Matsumura Y, Takeda T. Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study. J Imaging Inform Med. 2025 Jan 22. Epub ahead of print.

2024

  1. Wada S, Takeda T, Okada K, Manabe S, Konishi S, Kamohara J, Matsumura Y. Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT. Artif Intell Med. 2024 Jul;153:102889. Epub 2024 May 5. PMID: 38728811.

2023

  1. Kodama K, Konishi S, Manabe S, Okada K, Yamaguchi J, Wada S, Sugimoto K, Itoh S, Takahashi D, Kawasaki R, Matsumura Y, Takeda T. Impact of an Electronic Medical Record-Connected Questionnaire on Efficient Nursing Documentation: Usability and Efficacy Study. JMIR Nurs. 2023 Sep 25;6:e51303.

  2. Sugimoto K, Wada S, Konishi S, Okada K, Manabe S, Matsumura Y, Takeda T. Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation. JMIR Med Inform. 2023 Nov 14;11:e49041.

2021

  1. Sugimoto K, Takeda T, Oh JH, Wada S, Konishi S, Yamahata A, Manabe S, Tomiyama N, Matsunaga T, Nakanishi K, Matsumura Y. Extracting clinical terms from radiology reports with deep learning. J Biomed Inform. 2021 Apr;116:103729. Epub 2021 Mar 9.

  2. Wang B, Takeda T, Sugimoto K, Zhang J, Wada S, Konishi S, Manabe S, Okada K, Matsumura Y. Automatic creation of annotations for chest radiographs based on the positional information extracted from radiographic image reports. Comput Methods Programs Biomed. 2021 Sep;209:106331.Epub 2021 Aug 4.

2018

  1. Kawahara Y, Wada S, Nijima H, Hayase T, Furukawa R, Ashizawa K, Morimoto A. Rhinocerebral Mucormycosis With Temporal Artery Thrombosis in an Adolescent Following HLA-haploidentical Stem Cell Transplantation. J Pediatr Hematol Oncol. 2018 Oct;40(7):e461-e463.

2016

  1. Wada S, Kumagai H, Yokoyama K, Ito T, Miyauchi A, Sakamoto S, Imagawa T, Tulyeu J, Tanaka M, Yamagata T. Mesalazine allergy in a boy with ulcerative colitis: clinical usefulness of mucosal biopsy criteria. Clin J Gastroenterol. 2016 Oct;9(5):302-5. Epub 2016 Aug 8.

Conference Papers

International Conferences

2025

  1. “Improving MedDRA/J Coding Accuracy with a Fine-Tuned Text Embedding Model”

2024

  1. Okada K, Sugimoto K, Wada S, Konishi S, Manabe S, Matsumura Y, Takeda T. Development of the Clinical Specimen Information Management System for Multicenter Clinical Studies. Stud Health Technol Inform. 2024 Jan 25;310:119-123.

  2. Konishi S, Manabe S, Shimai Y, Namiuchi Y, Sugimoto K, Wada S, Okada K, Matsumura Y, Takeda T. Implementation of a Multilingual Electronic Medical Questionnaire: For Use Inside or Outside the Hospital. Stud Health Technol Inform. 2024 Jan 25;310:1360-1361.

  3. Sugimoto K, Wada S, Konishi S, Okada K, Manabe S, Matsumura Y, Takeda T. Classification of Diagnostic Certainty in Radiology Reports with Deep Learning. Stud Health Technol Inform. 2024 Jan 25;310:569-573. doi: . PMID: 38269873.

  4. Sugimoto K, Wada S, Konishi S, Okada K, Nakashima K, Matsumura Y, Takeda T. Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing. Stud Health Technol Inform. 2024 Aug 22;316:1795-1799.

2020

  1. Sugimoto K, Takeda T, Wada S, Yamahata A, Konishi S, Manabe S, Matsumura Y. End-to-End Approach for Structuring Radiology Reports. Stud Health Technol Inform. 2020 Jun 16;270:203-207.

2019

  1. "Extracting Symptom Names and Disease-Symptom Relationships from Web Texts Using a Multi-Column Convolutional Neural Network

  2. Takeda T, Zhang D, Wada S, Nakagawa A, Sugimoto K, Manabe S, Matsumura Y. The Acquisition of Structured Clinical Data from a Document-Based Electronic Medical Record System. Stud Health Technol Inform. 2019 Aug 21;264:1600-1601.

最終更新: 2025年8月

References
  1. Sugimoto, K., Wada, S., Konishi, S., Sato, J., Okada, K., Kido, S., Tomiyama, N., Matsumura, Y., & Takeda, T. (2025). Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study. Journal of Imaging Informatics in Medicine. 10.1007/s10278-024-01338-w
  2. Wada, S., Takeda, T., Okada, K., Manabe, S., Konishi, S., Kamohara, J., & Matsumura, Y. (2024). Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT. Artificial Intelligence in Medicine, 153, 102889. 10.1016/j.artmed.2024.102889
  3. Kodama, K., Konishi, S., Manabe, S., Okada, K., Yamaguchi, J., Wada, S., Sugimoto, K., Itoh, S., Takahashi, D., Kawasaki, R., Matsumura, Y., & Takeda, T. (2023). Impact of an Electronic Medical Record–Connected Questionnaire on Efficient Nursing Documentation: Usability and Efficacy Study. JMIR Nursing, 6, e51303. 10.2196/51303
  4. Sugimoto, K., Wada, S., Konishi, S., Okada, K., Manabe, S., Matsumura, Y., & Takeda, T. (2023). Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation. JMIR Medical Informatics, 11, e49041–e49041. 10.2196/49041
  5. Sugimoto, K., Takeda, T., Oh, J.-H., Wada, S., Konishi, S., Yamahata, A., Manabe, S., Tomiyama, N., Matsunaga, T., Nakanishi, K., & Matsumura, Y. (2021). Extracting clinical terms from radiology reports with deep learning. Journal of Biomedical Informatics, 116, 103729. 10.1016/j.jbi.2021.103729
  6. Wang, B., Takeda, T., Sugimoto, K., Zhang, J., Wada, S., Konishi, S., Manabe, S., Okada, K., & Matsumura, Y. (2021). Automatic creation of annotations for chest radiographs based on the positional information extracted from radiographic image reports. Computer Methods and Programs in Biomedicine, 209, 106331. 10.1016/j.cmpb.2021.106331
  7. Kawahara, Y., Wada, S., Nijima, H., Hayase, T., Furukawa, R., Ashizawa, K., & Morimoto, A. (2018). Rhinocerebral Mucormycosis With Temporal Artery Thrombosis in an Adolescent Following HLA-haploidentical Stem Cell Transplantation. Journal of Pediatric Hematology/Oncology, 40(7), e461–e463. 10.1097/mph.0000000000001020
  8. Wada, S., Kumagai, H., Yokoyama, K., Ito, T., Miyauchi, A., Sakamoto, S., Imagawa, T., Tulyeu, J., Tanaka, M., & Yamagata, T. (2016). Mesalazine allergy in a boy with ulcerative colitis: clinical usefulness of mucosal biopsy criteria. Clinical Journal of Gastroenterology, 9(5), 302–305. 10.1007/s12328-016-0675-2
  9. Okada, K., Sugimoto, K., Wada, S., Konishi, S., Manabe, S., Matsumura, Y., & Takeda, T. (2024). Development of the Clinical Specimen Information Management System for Multicenter Clinical Studies. In MEDINFO 2023 — The Future Is Accessible. IOS Press. 10.3233/shti230939
  10. Sugimoto, K., Wada, S., Konishi, S., Okada, K., Nakashima, K., Matsumura, Y., & Takeda, T. (2024). Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing. In Digital Health and Informatics Innovations for Sustainable Health Care Systems. IOS Press. 10.3233/shti240779
  11. Konishi, S., Manabe, S., Shimai, Y., Namiuchi, Y., Sugimoto, K., Wada, S., Okada, K., Matsumura, Y., & Takeda, T. (2024). Implementation of a Multilingual Electronic Medical Questionnaire: For Use Inside or Outside the Hospital. In MEDINFO 2023 — The Future Is Accessible. IOS Press. 10.3233/shti231194
  12. Kento, S., Toshihiro, T., Shoya, W., Asuka, Y., Shozo, K., Shiro, M., & Yasushi, M. (2020). End-to-End Approach for Structuring Radiology Reports. In Digital Personalized Health and Medicine. IOS Press. 10.3233/shti200151
  13. Shoya, W., Ryu, I., Kentaro, T., Toshihiro, T., Shiro, M., & Yasushi, M. (2019). Extracting Symptom Names and Disease-Symptom Relationships from Web Texts Using a Multi-Column Convolutional Neural Network. In MEDINFO 2019: Health and Wellbeing e-Networks for All. IOS Press. 10.3233/shti190256
Research
Presentations & Posters
Presentations
Improving MedDRA/J Coding Accuracy with a Fine-Tuned Text Embedding Model