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

Pharmacovigilance (134) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (156) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Infodemiology and Infoveillance (1349) Natural Language Processing (692) Mass Media/Social Media Communication and Campaigns (461)

Published on in Vol 7, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29238, first published .
Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus

Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus

Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus

Authors of this article:

Shinichi Matsuda1 Author Orcid Image ;   Takumi Ohtomo1 Author Orcid Image ;   Shiho Tomizawa2 Author Orcid Image ;   Yuki Miyano1 Author Orcid Image ;   Miwako Mogi3 Author Orcid Image ;   Hiroshi Kuriki4 Author Orcid Image ;   Terumi Nakayama1 Author Orcid Image ;   Shinichi Watanabe1 Author Orcid Image
Article Authors Cited by (4) Tweetations (7) Metrics

Journals

  1. Wood T, McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Research 2023;7:56 View
  2. Yamazaki S, Izawa K, Matsushita M, Moriichi A, Kishida D, Yoshifuji H, Yamaji K, Nishikomori R, Mori M, Miyamae T. Promoting awareness of terminology related to unmet medical needs in context of rheumatic diseases in Japan: a systematic review for evaluating unmet medical needs. Rheumatology International 2023;43(11):2021 View
  3. Matsuda S, Ohtomo T, Okuyama M, Miyake H, Aoki K. Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation. JMIR Formative Research 2023;7:e48534 View
  4. Lázaro E, Yepez J, Marín-Maicas P, López-Masés P, Gimeno T, de Paúl S, Moscardó V. Efficiency of natural language processing as a tool for analysing quality of life in patients with chronic diseases. A systematic review. Computers in Human Behavior Reports 2024;14:100407 View