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IF 3.3 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making Pub Date : 2025-03-18 DOI:10.1186/s12911-025-02929-5

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

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引用次数: 0

摘要

背景：在这个在线交流活跃的时代，患者越来越多地通过数字平台分享他们的医疗保健经验、担忧和需求。利用这些庞大的现实世界信息库，数字聆听能够通过先进的技术系统地收集和分析患者的声音。语义- nlp人工智能能够从大量非结构化在线数据中处理和提取有意义的见解，代表了一种理解患者观点的新方法。本研究旨在证明语义nlp技术在韩国和台湾的老年性黄斑变性（AMD）患者的需求和关注方面的效用。方法：使用基于本体的信息提取系统（Semantic Hub）从2023年1月开始收集数据并进行分析。该系统从2013年1月至2023年3月的网络帖子中识别患者的“故事”并提取主题，通过过滤用户的地理位置、使用的语言和当地的在线平台，重点关注韩国和台湾。提取的文本被构建成知识图并进行描述性分析。结果：从韩国Naver在线平台的133857条消息（9620名患者）中识别出患者的声音，包括与黄斑变性有关的网络聊天论坛。影响AMD治疗的最重要因素是有效性（1632 / 3401）；48%)，价格和获得保险（33%），耐受性（10%）以及医生和诊所建议（9%）。玻璃体内注射血管内皮生长因子抑制剂的治疗负担与耐受性相关（254/942）；27%)、经济负担（20%）、医院选择（18%）和情感负担（14%）。在台湾，从Facebook、YouTube和Instagram上识别出了444条信息。判断治疗成功与否的标准是视力的改善(20/121次提及；16.5%)，对水肿的影响（10.7%），减少变形（9.1%）和抑制血管生成（5.8%）。很少提到耐受性问题(26/440次提及；5.9%)。结论：使用语义- nlp的数字听力可以快速地从大量互联网数据中提供真实世界的见解，并且人工成本低。这使得医疗保健公司能够在整个产品生命周期中满足患者对有效和安全治疗的未满足需求，并改善患者的生活质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.

Background: In 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.

Methods: Data 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.

Results: The 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%).

Conclusions: Digital 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.

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来源期刊

BMC Medical Informatics and Decision Making 医学-医学：信息

CiteScore

7.20

自引率

5.70%

发文量

297

审稿时长

1 months

期刊介绍： BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.