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

Adverse Drug Events Detection, Pharmacovigilance and Surveillance (156) Natural Language Processing (692) Pharmacovigilance (134)

Published on in Vol 6, No 3 (2018): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11021, first published .
Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese

Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese

Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese

Authors of this article:

Misa Usui1 Author Orcid Image ;   Eiji Aramaki2 Author Orcid Image ;   Tomohide Iwao2 Author Orcid Image ;   Shoko Wakamiya2 Author Orcid Image ;   Tohru Sakamoto3 Author Orcid Image ;   Mayumi Mochizuki4, 5 Author Orcid Image
Article Authors Cited by (16) Tweetations (2) Metrics

Journals

  1. Lin F, Huang S, Shang R, Wang C, Hsiao F, Lin F, Lin M, Hung K, Wang J, Shen L, Lai F, Huang C. A Web-Based Clinical System for Cohort Surveillance of Specific Clinical Effectiveness and Safety Outcomes: A Cohort Study of Non–Vitamin K Antagonist Oral Anticoagulants and Warfarin. JMIR Medical Informatics 2019;7(3):e13329 View
  2. Jourquin J, Reffey S, Jernigan C, Levy M, Zinser G, Sabelko K, Pietenpol J, Sledge G. Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes. JCO Precision Oncology 2019;(3):1 View
  3. Luo Y, Sun W, Rumshisky A. MCN: A comprehensive corpus for medical concept normalization. Journal of Biomedical Informatics 2019;92:103132 View
  4. Kersloot M, van Putten F, Abu-Hanna A, Cornet R, Arts D. Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies. Journal of Biomedical Semantics 2020;11(1) View
  5. Ujiie S, Yada S, Wakamiya S, Aramaki E. Identification of Adverse Drug Event–Related Japanese Articles: Natural Language Processing Analysis. JMIR Medical Informatics 2020;8(11):e22661 View
  6. Shin H, Cha J, Lee C, Song H, Jeong H, Kim J, Lee S. The 2011–2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance. Applied Sciences 2021;11(5):2249 View
  7. McKenzie J, Rajapakshe R, Shen H, Rajapakshe S, Lin A. A Semiautomated Chart Review for Assessing the Development of Radiation Pneumonitis Using Natural Language Processing: Diagnostic Accuracy and Feasibility Study. JMIR Medical Informatics 2021;9(11):e29241 View
  8. 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
  9. 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
  10. 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
  11. Ishida M, Yanaka H, Bekki D. Medc2l: Compound-Word Analysis and Inference System for Japanese Clinical Texts. Journal of Natural Language Processing 2023;30(3):935 View
  12. 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
  13. 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
  14. Nishioka S, Watabe S, Yanagisawa Y, Sayama K, Kizaki H, Imai S, Someya M, Taniguchi R, Yada S, Aramaki E, Hori S. Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models. Journal of Medical Internet Research 2024;26:e55794 View
  15. Ohno Y, Kato R, Ishikawa H, Nishiyama T, Isawa M, Mochizuki M, Aramaki E, Aomori T. Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis. JMIR Formative Research 2024;8:e55798 View

Books/Policy Documents

  1. . Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities. View