Evidence map›Paper›PMID 40102785›Full record

ArticleBMC medical informatics and decision making2025

Real-world insights of patient voices with age-related macular degeneration in the Republic of Korea and Taiwan: an AI-based Digital Listening study by Semantic-Natural Language Processing.

Hyewon Jeon, Su-Yeon Yu, Olga Chertkova, Hyejung Yun, Yi Lin Ng, Yan Yoong Lim, Irina Efimenko, Djoubeir Mohamed Makhlouf

Abstract read
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Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

The trial behind it

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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

1 citing paper in PubMed.

  1. 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

8 authors.

Hyewon JeonRoche Product Development Safety Risk Management, Roche Products Pty Limited, Sydney, Australia.ORCID 0009-0003-3267-1941
Su-Yeon YuDepartment of Pharmacy, College of Pharmacy, Kangwon National University, Chuncheon, Republic of Korea. suyeon.yu@kangwon.ac.kr.ORCID 0000-0001-5488-5068
Olga ChertkovaRoche Product Development Safety Risk Management, Roche Products Limited, Welwyn Garden City, UK.ORCID 0009-0004-9405-5316
Hyejung YunRoche Product Development Safety Risk Management, Roche Korea Company Ltd, Seoul, Republic of Korea.ORCID 0009-0005-3039-8165
Yi Lin NgRoche Product Development Safety Risk Management, Roche (Malaysia) Sdn. Bhd., Subang Jaya, Malaysia.ORCID 0009-0007-1036-7374
Yan Yoong LimRoche Product Development Safety Risk Management, Roche Hong Kong Limited, Kowloon Bay, Hong Kong SAR.ORCID 0009-0003-4421-1299
Irina EfimenkoSemantic Hub SA, Lausanne, Switzerland.ORCID 0000-0002-6167-8187
Djoubeir Mohamed MakhloufRoche Product Development Safety Risk Management, F. Hoffmann-La Roche AG, Basel, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this era of active online communication, patients increasingly share their healthcare experiences, concerns, and needs across digital platforms. Leveraging these vast repositories of real-world information, Digital Listening enables the systematic collection and analysis of patient voices through advanced technologies. Semantic-NLP artificial intelligence, with its ability to process and extract meaningful insights from large volumes of unstructured online data, represents a novel approach for understanding patient perspectives. This study aimed to demonstrate the utility of Semantic-NLP technology in presenting the needs and concerns of patients with age-related macular degeneration (AMD) in Korea and Taiwan.

methodsData were collected and analysed over three months from January 2023 using an ontology-based information extraction system (Semantic Hub). The system identified patient "stories" and extracted themes from online posts from January 2013 to March 2023, focusing on Korea and Taiwan by filtering the geographic location of users, the language used, and the local online platforms. Extracted texts were structured into knowledge graphs and analysed descriptively.

resultsThe patient voice was identified in 133,857 messages (9,620 patients) from the Naver online platform in Korea and included internet chat forums focused on macular degeneration. The most important factors for AMD treatments were effectiveness (1,632/3,401 mentions; 48%), price and access to insurance (33%), tolerability (10%) and doctor and clinic recommendations (9%). Treatment burden associated with intravitreal injection of vascular endothelial growth factor inhibitors related to tolerability (254/942 mentions; 27%), financial burden (20%), hospital selection (18%) and emotional burden (14%). In Taiwan, 444 messages were identified from Facebook, YouTube and Instagram. The success of treatment was judged by improvements in visual acuity (20/121 mentions; 16.5%), effect on oedema (10.7%), less distortion (9.1%) and inhibition of angiogenesis (5.8%). Tolerability concerns were rarely mentioned (26/440 mentions; 5.9%).

conclusionsDigital Listening using Semantic-NLP can provide real-world insights from large amounts of internet data quickly and with low human labour cost. This allows healthcare companies to respond to the unmet needs of patients for effective and safe treatment and improved patient quality of life throughout the product lifecycle.

Indexed as

Artificial IntelligenceMacular DegenerationNatural Language ProcessingHumansRepublic of KoreaSemanticsTaiwanDigitalMacular degenerationNatural language processingPatient voiceReal-world dataSemantics/semantic analysisSocial mediaTolerabilityUnmet needs

Identifiers

PMID40102785
PMCPMC11916980

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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.