This paper is in the following e-collection/theme issue:

Adverse Drug Events Detection, Pharmacovigilance and Surveillance (159) Natural Language Processing (706)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22661, first published .
Identification of Adverse Drug Event–Related Japanese Articles: Natural Language Processing Analysis

Identification of Adverse Drug Event–Related Japanese Articles: Natural Language Processing Analysis

Identification of Adverse Drug Event–Related Japanese Articles: Natural Language Processing Analysis

Authors of this article:

Shogo Ujiie1 Author Orcid Image ;   Shuntaro Yada1 Author Orcid Image ;   Shoko Wakamiya1 Author Orcid Image ;   Eiji Aramaki1 Author Orcid Image
Article Authors Cited by (11) Tweetations (5) Metrics

Journals

  1. Grabar N, Grouin C. Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing. Yearbook of Medical Informatics 2021;30(01):257 View
  2. Kaas‐Hansen B, Placido D, Rodríguez C, Thorsen‐Meyer H, Gentile S, Nielsen A, Brunak S, Jürgens G, Andersen S. Language‐agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records. Basic & Clinical Pharmacology & Toxicology 2022;131(4):282 View
  3. Hamamoto R, Koyama T, Kouno N, Yasuda T, Yui S, Sudo K, Hirata M, Sunami K, Kubo T, Takasawa K, Takahashi S, Machino H, Kobayashi K, Asada K, Komatsu M, Kaneko S, Yatabe Y, Yamamoto N. Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information. Experimental Hematology & Oncology 2022;11(1) View
  4. Teramoto K, Takeda T, Mihara N, Shimai Y, Manabe S, Kuwata S, Kondoh H, Matsumura Y. Detecting Adverse Drug Events Through the Chronological Relationship Between the Medication Period and the Presence of Adverse Reactions From Electronic Medical Record Systems: Observational Study. JMIR Medical Informatics 2021;9(11):e28763 View
  5. Bhatnagar R, Sardar S, Beheshti M, Podichetty J. How can natural language processing help model informed drug development?: a review. JAMIA Open 2022;5(2) View
  6. Mashima Y, Tamura T, Kunikata J, Tada S, Yamada A, Tanigawa M, Hayakawa A, Tanabe H, Yokoi H. Using Natural Language Processing Techniques to Detect Adverse Events From Progress Notes Due to Chemotherapy. Cancer Informatics 2022;21 View
  7. Binsfeld Gonçalves L, Nesic I, Obradovic M, Stieltjes B, Weikert T, Bremerich J. Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame. JMIR Medical Informatics 2022;10(12):e40534 View
  8. Yamanouchi Y, Nakamura T, Ikeda T, Usuku K. An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records. Methods of Information in Medicine 2023;62(03/04):110 View
  9. Maeda-Minami A, Yoshino T, Yumoto T, Sato K, Sagara A, Inaba K, Kominato H, Kimura T, Takishita T, Watanabe G, Nakamura T, Mano Y, Horiba Y, Watanabe K, Kamei J. Development of a novel drug information provision system for Kampo medicine using natural language processing technology. BMC Medical Informatics and Decision Making 2023;23(1) View
  10. Mashima Y, Tanigawa M, Yokoi H. Information heterogeneity between progress notes by physicians and nurses for inpatients with digestive system diseases. Scientific Reports 2024;14(1) View
  11. Yada S, Nishiyama T, Wakamiya S, Kawazoe Y, Imai S, Hori S, Aramaki E, Pinto J. Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs. PLOS ONE 2024;19(9):e0310432 View