Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model.

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS
Hocheol Lee, Yu Seong Hwang, Ye Jun Kim, Yukyung Park, Heui Sug Jo
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引用次数: 0

Abstract

Background: Cardiovascular and cerebrovascular diseases significantly contribute to global mortality and disability. The shift to outpatient postoperative care, accelerated by the COVID-19 pandemic, emphasizes the need for effective management of postoperative outcomes. The high rates of cardiovascular and cerebrovascular diseases in Korea necessitate focused transitional care during patient discharge periods. However, limited research exists on the postoperative experiences of discharged patients, underscoring the necessity of establishing evidence-based services to optimize transitional care.

Objective: The objective of this paper was to analyze the emotional experiences of patients who underwent cardiovascular and cerebrovascular surgeries using data from Naver, a major South Korean web portal.

Methods: Posts were collected using specific keywords and processed with the Korean Bidirectional Encoder Representations from Transformers (KoBERT) model based on Transformer, which classified sentiments into positive, neutral, and negative categories. Model performance was validated according to precision, recall, F1-score, and support. Sentiment analysis was conducted within the Transitional Care Model (TCM) framework, divided into 5 domains: health status, care resources, care demand, interaction, and mental state.

Results: The KoBERT model demonstrated high classification performance, achieving a precision of 96%, recall of 94%, and an F1-score of 94%. Sentiment analysis revealed that compared with cardiovascular surgery patients, cerebrovascular surgery patients experienced higher negative emotions regarding health status, whereas cardiovascular surgery patients expressed more negative sentiments in care demands.

Conclusions: Different patient groups experience distinct emotional and practical challenges postdischarge. Particularly, keywords within the TCM framework highlight that cerebrovascular surgery patients require robust rehabilitation and caregiver support, whereas cardiovascular surgery patients need better cost management. These findings underscore the importance of personalized transitional care strategies tailored for cardiovascular and cerebrovascular diseases. The insights derived from this study can guide health care policymakers in designing more targeted and patient-centered interventions to improve postdischarge care and patient-centered transitional care, ensuring continuous and effective postoperative management.

心脑血管疾病手术患者的经验:使用韩国双向编码器表示从变压器(KoBERT)模型的情绪分析。
背景:心脑血管疾病是全球死亡和残疾的重要原因。COVID-19大流行加速了向门诊术后护理的转变,这强调了对术后结果进行有效管理的必要性。韩国的心脑血管疾病发病率很高,因此需要在病人出院期间进行集中的过渡性护理。然而,关于出院患者术后体验的研究有限,强调了建立循证服务以优化过渡护理的必要性。目的:本文的目的是利用韩国主要门户网站Naver的数据分析接受心脑血管手术的患者的情绪体验。方法:根据特定关键词收集帖子,采用基于Transformer的韩国双向编码器表示(KoBERT)模型进行处理,将情绪分为积极、中性和消极三类。根据准确率、召回率、f1评分和支持度验证模型的性能。在过渡期护理模式(TCM)框架下进行情绪分析,分为健康状况、护理资源、护理需求、互动和心理状态5个领域。结果:KoBERT模型具有较高的分类性能,准确率为96%,召回率为94%,f1得分为94%。情绪分析显示,与心血管手术患者相比,脑血管手术患者对健康状况的负性情绪较高,而心血管手术患者对护理需求的负性情绪较高。结论:不同的患者群体在出院后经历不同的情绪和实际挑战。特别是,中医框架内的关键词强调脑血管手术患者需要强有力的康复和护理支持,而心血管手术患者需要更好的成本管理。这些发现强调了为心脑血管疾病量身定制个性化过渡护理策略的重要性。本研究得出的见解可以指导卫生保健决策者设计更有针对性和以患者为中心的干预措施,以改善出院后护理和以患者为中心的过渡护理,确保持续有效的术后管理。
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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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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