Evidence map›Paper›PMID 40068144›Full record

ArticleJMIR cancer2025

Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition.

Yuki Yanagisawa, Satoshi Watabe, Sakura Yokoyama, Kyoko Sayama, Hayato Kizaki, Masami Tsuchiya, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada and 2 more

Abstract read
In one paragraph

Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yuki YanagisawaDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0000-0001-6998-6654
Satoshi WatabeDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0009-0000-1638-0579
Sakura YokoyamaDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0009-0000-6723-1047
Kyoko SayamaDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0009-0002-0394-3269
Hayato KizakiDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0000-0002-4572-1333
Masami TsuchiyaDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0000-0003-3846-0435
Shungo ImaiDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0000-0001-5706-613X
Mitsuhiro SomeyaNakajima Pharmacy, Hokkaido, Japan.ORCID 0009-0005-1090-6871
Ryoo TaniguchiNakajima Pharmacy, Hokkaido, Japan.ORCID 0009-0001-2292-666X
Shuntaro YadaNara Institute of Science and Technology, Nara, Japan.ORCID 0000-0002-6209-1054
Eiji AramakiNara Institute of Science and Technology, Nara, Japan.ORCID 0000-0003-0201-3609
Satoko HoriDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.ORCID 0000-0002-4596-5418

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAndrogen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists.

objectiveThis study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records.

methodsWe used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs.

resultsThe F

conclusionsThe NER system successfully extracted AEs from pharmaceutical care records of patients prescribed ARATs, demonstrating its potential to systematically track the presence and absence of AEs in outpatients. Based on the analysis of a large volume of pharmaceutical medical records using the NER system, community pharmacists not only detect potential AEs but also actively monitor the absence of severe AEs, offering valuable insights for the continuous improvement of patient safety management.

Indexed as

Community Pharmacy ServicesDrug-Related Side Effects and Adverse ReactionsProstatic NeoplasmsProstatic Neoplasms, Castration-ResistantAgedHumansJapanMaleMiddle AgedOutpatientsPharmaciesPharmacistsadverse eventsandrogen receptor axis-targeting agentsnatural language processingoutpatient carepharmaceutical care records

Identifiers

PMID40068144
PMCPMC11937706

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.