{"id":268,"date":"2023-07-15T02:04:52","date_gmt":"2023-07-14T17:04:52","guid":{"rendered":"https:\/\/sociocom.naist.jp\/nlp-crest\/?page_id=268"},"modified":"2025-06-29T18:16:28","modified_gmt":"2025-06-29T09:16:28","slug":"performance","status":"publish","type":"page","link":"https:\/\/sociocom.naist.jp\/nlp-crest\/performance\/","title":{"rendered":"\u696d\u7e3e"},"content":{"rendered":"\t\t
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FY 2025<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\u56fd\u969b\u4f1a\u8b70<\/h3>
  1. Masami Tsuchiya, Kiminori Shimamoto, Yoshimasa Kawazoe, Emiko Shinohara, Shuntaro Yada, Shoko Wakamiya, Shungo Imai, Hayato Kizaki, Satoko Hori, and Eiji Aramaki: Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis, MEDINFO2025 (Oral) (2025\/8\/13, Taipei, Taiwan) (to appear)<\/span><\/li>
  2. Takeshi Ito, Tomohide Yoshie, Sohei Yoshimura, Nobuyuki Ohara, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki: Large Language Models can be a good medical annotator: A case study of drug change detection in Japanese EHRs, MEDINFO2025 (Oral) (2025\/8\/13, Taipei, Taiwan) (to appear)<\/span><\/li>
  3. Tomohiro Nishiyama, Shuntaro Yada, Shoko Wakamiya, Satoko Hori, and Eiji Aramaki: Monitoring Over-The-Counter Drug Use from Japanese User-Generated Data, MEDINFO2025 (Oral) (2025\/8\/13, Taipei, Taiwan) (to appear)<\/span><\/li>
  4. Yuka Otsuki, Shuntaro Yada, Tomohiro Nishiyama, Toshiyuki Sakurai, Masafumi Okada, Noriko Kudo, Kyoko Kawabata, Takako Fujimaki, Hiroyuki Nagai, Shoko Wakamiya, and Eiji Aramaki: Efficient Maintenance of Large-Scale Medical Dictionaries Using Large Language Models: A Case for Biomarkers, MEDINFO2025 (Oral) (2025\/8\/12, Taipei, Taiwan) (to appear)<\/span><\/li>
  5. Jude Crener Junior Pierre, Gabriel Herman Bernardim Andrade, Shaowen Peng, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki: Abbreviation Expansion using only a Single Word as Context, MEDINFO2025 (Poster) (2025\/8\/12, Taipei, Taiwan) (to appear)<\/span><\/li>
  6. Seiji Shimizu, Ibrahim Baroud, Lisa Raithel, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki: RecordTwin: Towards Creating Safe Synthetic Clinical Corpora, In Findings of the Association for Computational Linguistics: ACL 2025 (Poster) (2025\/7\/29, Vienna, Austria) (to appear)<\/span><\/li>
  7. Seiji Shimizu, Shohei Hisada, Yutaka Uno, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki: Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions, In Findings of the Association for Computational Linguistics: ACL 2025 (Poster) (2025\/7\/29, Vienna, Austria) (to appear)<\/span><\/li><\/ol>

    \u56fd\u5185\u4f1a\u8b70<\/h3>
    1. \u4f0a\u85e4 \u5065\uff0c\u5409\u6c5f \u667a\u79c0\uff0c\u5409\u6751 \u58ee\u5e73\uff0c\u5c3e\u539f \u4fe1\u884c\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff1a\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u65e5\u672c\u8a9e\u96fb\u5b50\u30ab\u30eb\u30c6\u306e\u6cbb\u7642\u4e2d\u6b62\u7406\u7531\u5206\u985e\uff0c2025\u5e74\u5ea6 \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08\u7b2c39\u56de\uff09\uff0c4A1-GS-10-02\uff08\u53e3\u982d\uff09\uff082025\/5\/30, \u5927\u962a\u56fd\u969b\u4f1a\u8b70\u5834\uff09<\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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      FY 2024<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      \u5b66\u8853\u8ad6\u6587\u8a8c<\/h2>
      1. Tatsuki Hasegawa, Hayato Kizaki, Shungo Imai, Yuki Yanagisawa, Shuntaro Yada, Eiji Aramaki, and Satoko Hori: Improving Systematic Review Updates with Natural Language Processing: A Study on Screening Model Efficiency Through Component Classification and Selection, JMIR Medical Informatics 2025;13:e65371 (2025\/3\/27) [OPEN ACCESS<\/a>] (IF 3.1)<\/li>
      2. Yuki Yanagisawa, Satoshi Watabe, Sakura Yokoyama, Kyoko Sayama, Hayato Kizaki, Masami Tsuchiya, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, and Satoko Hori: Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition, JMIR Cancer 2025;11:e69663 (2025\/3\/11) [OPEN ACCESS<\/a>] (IF 3.3)<\/li>
      3. Yoshimasa Kawazoe, Masami Tsuchiya, Kiminori Shimamoto, Tomohisa Seki, Emiko Shinohara, Shuntaro Yada, Shoko Wakamiya, Shungo Imai, Eiji Aramaki, and Satoko Hori: Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity, Scientific Reports, 15:6678 (2025\/2\/24) [OPEN ACCESS<\/a>] (IF 3.8)<\/li>
      4. Takuya Fukushima, Masae Manabe, Shuntaro Yada, Shoko Wakamiya, Akiko Yoshida, Yusaku Urakawa, Akiko Maeda, Shigeyuki Kan, Masayo Takahashi, and Eiji Aramaki: Evaluating and Enhancing Japanese Large Language Models for Genetic Counseling Support: Comparative Study of Domain Adaptation and the Development of an Expert-Evaluated Dataset, JMIR Medical Informatics 2025;13:e65047 (2025\/1\/16) [OPEN ACCESS<\/a>] (IF 3.1)<\/li>
      5. Yoshimasa Kawazoe, Kiminori Shimamoto, Tomohisa Seki, Masami Tsuchiya, Emiko Shinohara, Shuntaro Yada, Shoko Wakamiya, Shungo Imai, Satoko Hori, and Eiji Aramaki: Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records, npj Digital Medicine, 7(315) (2024\/11\/9) [OPEN ACCESS<\/a>] (IF 12.4)<\/li>
      6. Shuntaro Yada, Tomohiro Nishiyama, Shoko Wakamiya, Yoshimasa Kawazoe, Shungo Imai, Satoko Hori, Eiji Aramaki<\/span>: Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs,\u00a0PLoS ONE 19(9): e0310432 (2024\/9\/11) [OPEN ACCESS<\/a>] (IF 3.1)<\/span><\/li>
      7. Gabriel Herman Bernardim Andrad, Shuntaro Yada, Eiji Aramaki: Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study, JMIR Medical Informatics, 12:e59680, 2024 (2024\/7\/2) [OPEN ACCESS<\/a>] (IF 3.1)<\/li>
      8. Zhouqing Zhang, Kongmeng Liew, Roeline Kuijer, Wan Jou She, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki: Differing Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer, JMIR Cancer, 10:e51332, 2024 (2024\/5\/9) [OPEN ACCESS<\/a>] (IF 2.8)<\/li><\/ol>

        \u56fd\u969b\u4f1a\u8b70<\/h3>
        1. Seiji Shimizu, Shuntaro Yada, Lisa Raithel, Eiji Aramaki: Improving Self-training with Prototypical Learning for Source-Free Domain Adaptation on Clinical Text<\/a>, In Proceedings of the 23rd Workshop on Biomedical Language Processing (BioNLP), pp. 1-13 (2024\/8\/16, Bangkok, Thailand)<\/li>
        2. Seiji Shimizu, Lis Pereira, Shuntaro Yada, Eiji Aramaki: QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation<\/a>, In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 13371-13381 (2024\/5\/24, Torino, Italy)<\/li><\/ol>

          \u56fd\u5185\u4f1a\u8b70<\/h3>
          1. \u5927\u69fb \u512a\u4f73\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u897f\u5c71 \u667a\u5f18\uff0c\u5de5\u85e4 \u7d00\u5b50\uff0c\u5ddd\u7aef \u4eac\u5b50\uff0c\u85e4\u7267 \u8cb4\u5b50\uff0c\u6c38\u4e95 \u5ba5\u4e4b\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff1a\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u3092\u6d3b\u7528\u3057\u305f\u5927\u898f\u6a21\u533b\u7642\u7528\u8a9e\u8f9e\u66f8\u30e1\u30f3\u30c6\u30ca\u30f3\u30b9\u306e\u52b9\u7387\u5316\uff0c\u8a00\u8a9e\u51e6\u7406\u5b66\u4f1a\u7b2c31\u56de\u5e74\u6b21\u5927\u4f1a (NLP2025)\uff0cB9-3\uff082025\/3\/13\uff0c\u51fa\u5cf6\u30e1\u30c3\u30bb\u9577\u5d0e\uff09<\/span><\/li>
          2. \u6a4b\u672c\u6e05\u6597\uff0c\u6e05\u6c34\u8056\u53f8 \uff0c\u5de5\u85e4\u7d00\u5b50\uff0c\u77e2\u7530\u7ae3\u592a\u90ce\uff0c\u82e5\u5bae\u7fd4\u5b50\uff0c\u6c5f\u672c\u99ff\uff0c\u897f\u6751\u7531\u5e0c\u5b50\uff0c\u8352\u7267\u82f1\u6cbb\uff1a\u8cea\u7684\u7814\u7a76\u306e\u81ea\u52d5\u5316:\u60a3\u8005\u81ea\u7531\u8a18\u8ff0\u30c6\u30ad\u30b9\u30c8\u304b\u3089\u306e\u6f5c\u5728\u7684\u30c8\u30d4\u30c3\u30af\u306e\u767a\u898b\uff0c\u8a00\u8a9e\u51e6\u7406\u5b66\u4f1a\u7b2c31\u56de\u5e74\u6b21\u5927\u4f1a (NLP2025)\uff0cP6-1\uff08\u30dd\u30b9\u30bf\u30fc\u767a\u8868\uff09\uff082025\/3\/12\uff0c\u51fa\u5cf6\u30e1\u30c3\u30bb\u9577\u5d0e\uff09<\/span><\/li>
          3. \u8fbb \u822a\u5e73\uff0c\u5e73\u5ca1 \u9054\u4e5f\uff0cCheng Yuchang\uff0c\u8352\u7267 \u82f1\u6cbb\uff0c\u5ca9\u5009 \u53cb\u54c9\uff1a\u8aa4\u5b57\u306b\u5bfe\u3059\u308bTransformer\u30d9\u30fc\u30b9LLM\u306e\u30cb\u30e5\u30fc\u30ed\u30f3\u304a\u3088\u3073\u30d8\u30c3\u30c9\u306e\u5f79\u5272\u8abf\u67fb\uff0c\u8a00\u8a9e\u51e6\u7406\u5b66\u4f1a\u7b2c31\u56de\u5e74\u6b21\u5927\u4f1a (NLP2025)\uff0cP2-11\uff08\u30dd\u30b9\u30bf\u30fc\u767a\u8868\uff09\uff082025\/3\/11\uff0c\u51fa\u5cf6\u30e1\u30c3\u30bb\u9577\u5d0e\uff09<\/li>
          4. \u67f3\u6fa4 \u53cb\u5e0c\uff0c\u6e21\u90e8 \u54f2\uff0c\u6a2a\u5c71 \u3055\u304f\u3089\uff0c\u6728\u5d0e \u901f\u4eba\uff0c\u4eca\u4e95 \u4fca\u543e\uff0c\u67d3\u8c37 \u5149\u6d0b\uff0c\u8c37\u53e3 \u4eae\u592e\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u8352\u7267 \u82f1\u6cbb\uff0c\u5800 \u91cc\u5b50\uff1a\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u6280\u8853\u3092\u6d3b\u7528\u3057\u305f\u85ac\u5c40\u85ac\u6b74\u306e\u75c7\u72b6\u8074\u53d6\u306b\u57fa\u3065\u304f\u30a2\u30bb\u30b9\u30e1\u30f3\u30c8\u5185\u5bb9\u306e\u53ef\u8996\u5316\uff0c\u7b2c34\u56de\u65e5\u672c\u533b\u7642\u85ac\u5b66\u4f1a\u5e74\u4f1a\uff0cP0454-3-PM1\uff08\u30dd\u30b9\u30bf\u30fc\u767a\u8868\uff09\uff082024\/11\/3\uff0c\u5e55\u5f35\u30e1\u30c3\u30bb \u56fd\u969b\u5c55\u793a\u5834\uff09<\/span><\/li>
          5. \u571f\u5c4b \u96c5\u7f8e\uff0c\u5d8b\u672c \u516c\u5fb3\uff0c\u6cb3\u6dfb \u60a6\u660c\uff0c\u7be0\u539f \u6075\u7f8e\u5b50\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u4eca\u4e95 \u4fca\u543e\uff0c\u6728\u5d0e \u901f\u4eba\uff0c\u5800 \u91cc\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff1a\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u3092\u7528\u3044\u305f\u8a3a\u7642\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u304b\u3089\u306e\u304c\u3093\u85ac\u7269\u7642\u6cd5\u526f\u4f5c\u7528\u691c\u51fa\u3068\u53ef\u8996\u5316\u624b\u6cd5\u306e\u63d0\u6848\uff0c\u7b2c34\u56de\u65e5\u672c\u533b\u7642\u85ac\u5b66\u4f1a\u5e74\u4f1a\uff0cP0317-2-PM\uff08\u30dd\u30b9\u30bf\u30fc\u767a\u8868\uff09\uff082024\/11\/2\uff0c\u5e55\u5f35\u30e1\u30c3\u30bb \u56fd\u969b\u5c55\u793a\u5834\uff09<\/span><\/li>
          6. Yukiko Ohno, Tohru Aomori, Riri Kato, Yuma Hikita, Reina Fujiki, Haruki Ishikawa, Keisuke Kiyomiya, Minae Isawa, Mayumi Mochizuki, Tomohiro Nishiyama, Eiji Aramaki, Hisakazu Ohtani: Extraction of diseases and symptoms from patients\u2019complaints using natural language processing system, \u7b2c34\u56de\u65e5\u672c\u533b\u7642\u85ac\u5b66\u4f1a\u5e74\u4f1a\uff0cIO-04<\/span>\uff082024\/11\/2\uff0c\u5e55\u5f35\u30e1\u30c3\u30bb \u56fd\u969b\u4f1a\u8b70\u5834\uff09\u512a\u79c0\u6f14\u984c\u8cde<\/strong><\/span><\/li>
          7. \u4e95\u4e0a\u771f\u7406\uff0c\u571f\u5c4b\u96c5\u7f8e\uff0c\u5d8b\u672c\u516c\u5fb3\uff0c\u6cb3\u6dfb\u60a6\u660c\uff0c\u7be0\u539f\u6075\u7f8e\u5b50\uff0c\u77e2\u7530\u7ae3\u592a\u90ce\uff0c\u82e5\u5bae\u7fd4\u5b50\uff0c\u4eca\u4e95\u4fca\u543e\uff0c\u6728\u5d0e\u901f\u4eba\uff0c\u5800\u91cc\u5b50\uff0c\u8352\u7267\u82f1\u6cbb\uff1a\u8a3a\u7642\u8a18\u9332\u3092\u6d3b\u7528\u3057\u305f\u30d5\u30c3\u5316\u30d4\u30ea\u30df\u30b8\u30f3\u7cfb\u6297\u304c\u3093\u85ac\u8a98\u767a\u6027\u53e3\u5185\u708e\u306b\u5bfe\u3059\u308bAT2\u53d7\u5bb9\u4f53\u62ee\u6297\u85ac\u306e\u4e88\u9632\u52b9\u679c\u306e\u691c\u8a3c\uff0c\u7b2c34\u56de\u65e5\u672c\u533b\u7642\u85ac\u5b66\u4f1a\u5e74\u4f1a\uff0c2-7-O03*-5\uff082024\/11\/2\uff0c\u5e55\u5f35\u30e1\u30c3\u30bb \u56fd\u969b\u4f1a\u8b70\u5834\uff09\u512a\u79c0\u6f14\u984c\u8cde<\/strong><\/span><\/li>
          8. \u571f\u5c4b\u96c5\u7f8e\uff0c\u6cb3\u6dfb\u60a6\u660c\uff0c\u5d8b\u672c\u516c\u5fb3\uff0c\u95a2\u502b\u4e45\uff0c\u4eca\u4e95\u4fca\u543e\uff0c\u6728\ufa11\u901f\u4eba\uff0c\u7be0\u539f\u6075\u7f8e\u5b50\uff0c\u77e2\u7530\u7ae3\u592a\u90ce\uff0c\u82e5\u5bae\u7fd4\u5b50\uff0c\u8352\u7267\u82f1\u6cbb\uff0c\u5800\u91cc\u5b50\uff1a\u8a3a\u7642\u8a18\u9332\u306b\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u3092\u7528\u3044\u305f\u30ab\u30da\u30b7\u30bf\u30d3\u30f3\u8a98\u767a\u6027\u624b\u8db3\u75c7\u5019\u7fa4\u306b\u5bfe\u3059\u308b\u30bb\u30ec\u30b3\u30ad\u30b7\u30d6\u306e\u4e88\u9632\u52b9\u679c\u306e\u691c\u8a3c\uff0c\u7b2c62\u56de\u65e5\u672c\u764c\u6cbb\u7642\u5b66\u4f1a\u5b66\u8853\u96c6\u4f1a\uff0cO53-3\uff082024\/10\/25\uff0c\u30de\u30ea\u30f3\u30e1\u30c3\u30bb\u798f\u5ca1\uff09<\/span><\/li>
          9. \u6e05\u6c34 \u8056\u53f8\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff1aRECORD TWIN: \u75c5\u6b74\u3092\u4fdd\u3061\u3064\u3064\u8868\u73fe\u304c\u7570\u306a\u308b\u75c7\u4f8b\u306e\u751f\u6210\uff0c\u60c5\u5831\u51e6\u7406\u5b66\u4f1a \u7b2c260\u56de \u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u7814\u7a76\u767a\u8868\u4f1a\uff082024\/6\/28\uff0c\u5317\u9678\u5148\u7aef\u79d1\u5b66\u6280\u8853\u5927\u5b66\u9662\u5927\u5b66 (JAIST) \u91d1\u6ca2\u99c5\u524d\u30aa\u30d5\u30a3\u30b9\uff09\u512a\u79c0\u7814\u7a76\u8cde<\/a><\/strong><\/li>
          10. \u798f\u5cf6 \u62d3\u4e5f\uff0c\u771e\u934b \u96c5\u6075\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff0c\u5409\u7530 \u6676\u5b50\uff0c\u6d66\u5ddd \u512a\u4f5c\uff0c\u524d\u7530 \u4e9c\u5e0c\u5b50\uff0c\u5bd2 \u91cd\u4e4b\uff0c\u9ad8\u6a4b \u653f\u4ee3\uff1aJGCLLM: \u65e5\u672c\u8a9e\u907a\u4f1d\u30ab\u30a6\u30f3\u30bb\u30ea\u30f3\u30b0\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\uff0c2024\u5e74\u5ea6 \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08\u7b2c38\u56de\uff09, 3S5-OS-7c-01\uff082024\/5\/30\uff0c\u30a2\u30af\u30c8\u30b7\u30c6\u30a3\u6d5c\u677e\uff09<\/span><\/li>
          11. \u771e\u934b \u96c5\u6075\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff1a\u30a8\u30d4\u30bd\u30fc\u30c9\u30d0\u30f3\u30af\uff1a\u5f53\u4e8b\u8005\u306e\u30ca\u30e9\u30c6\u30a3\u30d6\u3092\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u5316\u3059\u308b\u8a66\u307f\uff0c2024\u5e74\u5ea6 \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08\u7b2c38\u56de\uff09, 3S1-OS-7b-02\uff082024\/5\/30\uff0c\u30a2\u30af\u30c8\u30b7\u30c6\u30a3\u6d5c\u677e\uff09<\/span><\/li>
          12. \u4e45\u6751 \u98af\u6d77\uff0c\u4f50\u5c71 \u674f\u5b50\uff0c\u6e21\u90e8 \u54f2\uff0c\u6728\ufa11 \u901f\u4eba\uff0c\u4eca\u4e95 \u4fca\u543e\uff0c\u5cb8\u7530 \u5fb9\uff0c\u798f\u5ca1 \u5948\u6d25\u7f8e\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u8352\u7267 \u82f1\u6cbb\uff0c\u5800 \u91cc\u5b50\uff1a\u304c\u3093\u7d4c\u9a13\u8005\u30a4\u30f3\u30bf\u30d3\u30e5\u30fc\u30c6\u30ad\u30b9\u30c8\u3092\u7528\u3044\u305f\u304c\u3093\u60a3\u8005\u306e\u611f\u60c5\u5206\u6790\uff0c2024\u5e74\u5ea6 \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08\u7b2c38\u56de\uff09, 3S1-OS-7b-01\uff082024\/5\/30\uff0c\u30a2\u30af\u30c8\u30b7\u30c6\u30a3\u6d5c\u677e\uff09<\/span><\/li>
          13. \u6e21\u90e8 \u54f2\uff0c\u897f\u5ca1 \u6109\u53f2\uff0c\u67f3\u6fa4 \u53cb\u5e0c\uff0c\u4f50\u5c71 \u674f\u5b50\uff0c\u6728\ufa11 \u901f\u4eba\uff0c\u4eca\u4e95 \u4fca\u543e\uff0c\u67d3\u8c37 \u5149\u6d0b\uff0c\u8c37\u53e3 \u4eae\u592e\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u8352\u7267 \u82f1\u6cbb\uff0c\u5800 \u91cc\u5b50\uff1a\u6df1\u5c64\u5b66\u7fd2\u3092\u7528\u3044\u305f\u60a3\u8005\u4e3b\u8a34\u304b\u3089\u306e\u6709\u5bb3\u4e8b\u8c61\u30b7\u30b0\u30ca\u30eb\u691c\u51fa\u5668\u306e\u6709\u7528\u6027\u8a55\u4fa1\uff0c2024\u5e74\u5ea6 \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08\u7b2c38\u56de\uff09, 2S6-OS-7a-05\uff082024\/5\/29\uff0c\u30a2\u30af\u30c8\u30b7\u30c6\u30a3\u6d5c\u677e\uff09<\/span><\/li>
          14. \u5d8b\u672c \u516c\u5fb3\uff0c\u6cb3\u6dfb \u60a6\u660c\uff0c\u7be0\u539f \u6075\u7f8e\u5b50\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u4eca\u4e95 \u4fca\u543e\uff0c\u5800 \u91cc\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff1a\u96fb\u5b50\u30ab\u30eb\u30c6\u8a18\u9332\u304b\u3089\u306e\u533b\u85ac\u54c1\u6709\u5bb3\u4e8b\u8c61\u30b5\u30fc\u30d9\u30a4\u30e9\u30f3\u30b9\u624b\u6cd5\u306e\u958b\u767a\uff0c2024\u5e74\u5ea6 \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08\u7b2c38\u56de\uff09, 2S6-OS-7a-02\uff082024\/5\/29\uff0c\u30a2\u30af\u30c8\u30b7\u30c6\u30a3\u6d5c\u677e\uff09<\/span><\/li>
          15. \u5927\u69fb \u512a\u4f73\uff0c\u5927\u585a \u7687\u8f1d\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce\uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff0c\u5c3e\u539f \u4fe1\u884c\uff0c\u5409\u6c5f \u667a\u79c0\uff1a\u81e8\u5e8a\u7814\u7a76 DX \u306e\u8a66\u307f\uff1a\u8133\u5352\u4e2d\u30ea\u30b9\u30af\u56e0\u5b50\u306e\u30c6\u30ad\u30b9\u30c8\u69cb\u9020\u5316\u30b7\u30b9\u30c6\u30e0\uff0c2024\u5e74\u5ea6 \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08\u7b2c38\u56de\uff09, 2S6-OS-7a-01\uff082024\/5\/29\uff0c\u30a2\u30af\u30c8\u30b7\u30c6\u30a3\u6d5c\u677e\uff09<\/span><\/li><\/ol>

            \u53d7\u8cde<\/h3>
            1. 2024, \u7b2c34\u56de\u65e5\u672c\u533b\u7642\u85ac\u5b66\u4f1a\u5e74\u4f1a\uff0c\u512a\u79c0\u6f14\u984c\u8cde <\/strong>2\u4ef6<\/span><\/li>
            2. 2024, \u60c5\u5831\u51e6\u7406\u5b66\u4f1a \u7b2c260\u56de\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u7814\u7a76\u767a\u8868\u4f1a\uff0c\u512a\u79c0\u7814\u7a76\u8cde<\/a><\/strong><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
              \n\t\t\t\t\t\t
              \n\t\t\t\t\t
              \n\t\t\t
              \n\t\t\t\t\t\t
              \n\t\t\t\t
              \n\t\t\t\t\t

              FY 2022<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
              \n\t\t\t\t
              \n\t\t\t\t\t\t\t\t\t

              \u5b66\u8853\u8ad6\u6587\u8a8c<\/h2>\n
                \n
              1. Suzuki S, Imai S, Mitsuboshi S, Kizaki H, Hashiguchi M, Hori S: Detection of vaccine adverse events before package insert revisions using a Japanese spontaneous reporting system, J Clin Pharmacol, 10.1002\/jcph.2243, 2022. (2023\/04\/12) [OPEN ACCESS<\/a>]<\/li>\n
              2. Lean Franzl Lim Yao, Kiki Ferawati, Kongmeng Liew, Shoko Wakamiya, Eiji Aramaki: Disruptions in the Cystic Fibrosis Community\u2019s Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments, Journal of Medical Internet Research, 25:e45249, 2022. (2023\/04\/20) [OPEN ACCESS<\/a>]<\/li>\n
              3. Chatani R, Kondo S, Kamimura T, Suzuki S, Komatsubara E, Hayashi S, Hashiguchi M, Kizaki H, Imai S, Hori S: Exploring factors affecting the occurrence of hypersensitivity reactions induced by nonionic iodine contrast media, J Clin Pharmacol, 10.1002\/jcph.2256, 2022. (2023\/04\/28) [OPEN ACCESS<\/a>]<\/li>\n<\/ol>\n

                \u56fd\u969b\u4f1a\u8b70<\/h3>\n
                  \n
                1. Lean Franzl Lim Yao, Kongmeng Liew, Shoko Wakamiya, Eiji Aramaki: Extracting Spatio-Temporal Trends in Medical Research Prioritization Through Natural Language Processing of Case Report Abstracts, MedInfo 2023, 2022. (2022\/07\/10)<\/li>\n
                2. Faith Wavinya Mutinda, Kongmeng Liew, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki: PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature, In Proceedings of the Workshop on Information Extraction from Scientific Publications (WIESP 2022), 2022. (2022\/11\/21)<\/li>\n
                3. Nishioka S, Asano M, Yada S, Aramaki E, Yajima H, Kizaki H, Hori S: Detection of adverse event signals with severity grade classification from cancer patient narrative, MEDINFO2023, 2022. (2023\/07\/11) (accepted)<\/li>\n<\/ol>\n

                  \u56fd\u5185\u4f1a\u8b70<\/h3>\n
                    \n
                  1. \u6cb3\u6dfb \u60a6\u660c, \u6c38\u5cf6 \u91cc\u7f8e, \u5927\u6c5f \u548c\u5f66\uff1a\u30a2\u30ec\u30eb\u30ae\u30fc\u60c5\u5831\u306e\u6a19\u6e96\u5316\u3092\u76ee\u6307\u3059JFAGY\u30a2\u30ec\u30eb\u30b2\u30f3\u7528\u8a9e\u96c6\u3068\u30a2\u30ec\u30eb\u30b2\u30f3 \u30b3\u30fc\u30c9\u30b7\u30b9\u30c6\u30e0\uff0c\u7b2c42\u56de\u533b\u7642\u60c5\u5831\u9023\u5408\u5927\u4f1a, 2022.\uff082022\/11\/19\uff09<\/li>\n
                  2. \u67f4\u7530 \u5927\u4f5c, \u6cb3\u6dfb \u60a6\u660c, \u7be0\u539f \u6075\u7f8e\u5b50, \u5d8b\u672c \u516c\u5fb3\uff1a\u60a3\u8005\u72b6\u614b\u8868\u73fe\u306e\u75c5\u540d\u4ea4\u63db\u30b3\u30fc\u30c9\u3078\u306e\u30de\u30c3\u30d4\u30f3\u30b0\uff0c\u7b2c42\u56de\u533b\u7642\u60c5\u5831\u9023\u5408\u5927\u4f1a, 2022.\uff082022\/11\/20\uff09<\/li>\n
                  3. \u5e73\u6797(\u5bae\u90e8)\u771f\u8863, \u67f4\u7530 \u5927\u4f5c, \u7be0\u539f \u6075\u7f8e\u5b50, \u6cb3\u6dfb \u60a6\u660c\uff1a\u6d41\u8a00\u306e\u8a02\u6b63\u306fCOVID-19\u30ef\u30af\u30c1\u30f3\u63a5\u7a2e\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b?\uff0c\u30b0\u30eb\u30fc\u30d7\u30a6\u30a7\u30a2\u3068\u30cd\u30c3\u30c8\u30ef\u30fc\u30af \u30b5\u30fc\u30d3\u30b9\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7 2022(GNWS2022) , 2022. (2022\/11\/25)<\/li>\n
                  4. \u53e4\u8cc0 \u8cb4\u58eb\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce \uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u8352\u7267 \u82f1\u6cbb\uff1a\u81a8\u5927\u306a\u533b\u5b66\u77e5\u8b58\u3092\u7528\u3044\u305f\u30ab\u30eb\u30c6\u5165\u529b\u8aa4\u308a\u6587\u691c\u51fa\uff0c\u7b2c15\u56de\u30c7\u30fc\u30bf\u5de5\u5b66\u3068\u60c5\u5831\u30de\u30cd\u30b8 \u30e1\u30f3\u30c8\u306b\u95a2\u3059\u308b\u30d5\u30a9\u30fc\u30e9\u30e0 (DEIM2023) , 2022.  (2023\/03\/06)<\/li>\n
                  5. Zhouqing Zhang, Kongmeng Liew, Wanjou She, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki\uff1aText classification for breast cancer patient identity from Weibo posts\uff0c\u7b2c15\u56de\u30c7\u30fc\u30bf\u5de5\u5b66\u3068\u60c5\u5831\u30de\u30cd\u30b8 \u30e1\u30f3\u30c8\u306b\u95a2\u3059\u308b\u30d5\u30a9\u30fc\u30e9\u30e0 (DEIM2023) , 2022. (2023\/03\/06) \u5b66\u751f\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u8cde<\/strong><\/li>\n
                  6. \u897f\u5c71 \u667a\u5f18\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce \uff0c\u82e5\u5bae \u7fd4\u5b50\uff0c\u5800 \u91cc\u5b50\uff0c\u8352 \u7267 \u82f1\u6cbb\uff1a\u30bd\u30fc\u30b7\u30e3\u30eb\u30e1\u30c7\u30a3\u30a2\u3092\u7528\u3044\u305f\u4e00\u822c\u7528\u533b\u85ac\u54c1\u306e\u30d5\u30a1\u30fc\u30de\u30b3\u30d3\u30b8\u30e9\u30f3\u30b9\uff0c\u8a00\u8a9e\u51e6\u7406\u5b66\u4f1a \u7b2c29\u56de\u5e74\u6b21\u5927\u4f1a (NLP2023), pp.1755-1760, 2022. (2023\/03\/15)<\/li>\n
                  7. \u6e05\u6c34 \u8056\u53f8\uff0c\u77e2\u7530 \u7ae3\u592a\u90ce \uff0c\u8352\u7267 \u82f1\u6cbb\uff1aVariational Auto-Encoder \u306b\u3088\u308b\u75c7\u4f8b\u5831\u544a\u751f\u6210\uff0c\u8a00\u8a9e\u51e6\u7406\u5b66\u4f1a \u7b2c29\u56de\u5e74\u6b21\u5927\u4f1a (NLP2023), pp.2731-2736, 2022. (2023\/03\/16)<\/li>\n
                  8. \u6c96\u672c \u7dbe\u97f3\uff0c\u6728\ufa11 \u901f\u4eba\uff0c\u98ef \u91ce \u6e29\uff0c\u536f\u7530 \u5065\u592a\uff0c\u4eca\u4e95 \u4fca\u543e\uff0c\u7530\u4e2d \u8061\u4e00\uff0c\u68ee \u8cb4\u5e78 \uff0c\u5800 \u91cc\u5b50\uff1a\u85ac\u5c40\u3067\u306e\u75c7\u72b6\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0\u306b\u304a\u3051\u308b\u60a3\u8005\u75c7\u72b6\u805e\u304d\u53d6\u308a\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u6709\u7528\u6027\u8a55\u4fa1\uff0c\u65e5\u672c\u85ac\u5b66\u4f1a\u7b2c143\u5e74\u4f1a, 2022. (2023\/03\/27)<\/li>\n<\/ol>\n

                    \u53d7\u8cde<\/h3>\n
                      \n
                    1. 2022, \u7b2c15\u56de\u30c7\u30fc\u30bf\u5de5\u5b66\u3068\u60c5\u5831\u30de\u30cd\u30b8\u30e1\u30f3\u30c8\u306b\u95a2\u3059\u308b\u30d5\u30a9\u30fc\u30e9\u30e0 (DEIM2023) \u5b66\u751f\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u8cde<\/strong><\/li>\n
                    2. 2022, \u65e5\u672c\u85ac\u5b66\u4f1a\u7b2c143\u5e74\u4f1a \u5b66\u751f\u512a\u79c0\u767a\u8868\u8cde<\/strong><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

                      FY 2025 \u56fd\u969b\u4f1a\u8b70 Masami Tsuchiya, Kiminori Shimamoto, Yoshimasa Kawazoe, Emiko Shinohara, Shuntaro Yada, Shoko Wak… <\/p>\n","protected":false},"author":9,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"full-width","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"_links":{"self":[{"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/pages\/268"}],"collection":[{"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/comments?post=268"}],"version-history":[{"count":77,"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/pages\/268\/revisions"}],"predecessor-version":[{"id":453,"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/pages\/268\/revisions\/453"}],"wp:attachment":[{"href":"https:\/\/sociocom.naist.jp\/nlp-crest\/wp-json\/wp\/v2\/media?parent=268"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}