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

Artificial Intelligence (1755) Machine Learning (1750) Decision Support for Health Professionals (1347) Big Data (282) Safety and Error Prevention in Health (226) Natural Language Processing (804) Innovations and Technology in Cancer Care (466) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (187) Pharmacovigilance (155)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55794, first published .
Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Authors of this article:

Satoshi Nishioka1 Author Orcid Image ;   Satoshi Watabe1 Author Orcid Image ;   Yuki Yanagisawa1 Author Orcid Image ;   Kyoko Sayama1 Author Orcid Image ;   Hayato Kizaki1 Author Orcid Image ;   Shungo Imai1 Author Orcid Image ;   Mitsuhiro Someya2 Author Orcid Image ;   Ryoo Taniguchi2 Author Orcid Image ;   Shuntaro Yada3 Author Orcid Image ;   Eiji Aramaki3 Author Orcid Image ;   Satoko Hori1 Author Orcid Image
Article Authors Cited by (2) Tweetations (5) Metrics

Journals

  1. Hatem N. Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence. Integrated Pharmacy Research and Practice 2024;Volume 13:139 View
  2. Yanagisawa Y, Watabe S, Yokoyama S, Sayama K, Kizaki H, Tsuchiya M, Imai S, Someya M, Taniguchi R, Yada S, Aramaki E, Hori S. 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 View