Evidence map›Paper›PMID 40446313›Full record

ArticleJMIR medical informatics2025

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

Hocheol Lee, Yu Seong Hwang, Ye Jun Kim, Yukyung Park, Heui Sug Jo

Abstract read
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Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hocheol LeeDepartment of AI Health Information Management, Software Digital Healthcare Convergence College, Yonsei University, Wonju, Republic of Korea.ORCID 0000-0003-1467-8843
Yu Seong HwangDepartment of Health Policy and Management, School of Medicine, Kangwon National University, 510 School of Medicine Building #1 (N414), 1, Kangwondaehak-gil, Chuncheon, Gangwon-do, 24341, Republic of Korea, 82 332508910.ORCID 0000-0001-8934-3533
Ye Jun KimDivision of Data Science, Yonsei University, Wonju, Republic of Korea.ORCID 0009-0002-4381-7179
Yukyung ParkDepartment of Health Policy and Management, School of Medicine, Kangwon National University, 510 School of Medicine Building #1 (N414), 1, Kangwondaehak-gil, Chuncheon, Gangwon-do, 24341, Republic of Korea, 82 332508910.ORCID 0000-0002-5727-8973
Heui Sug JoDepartment of Health Policy and Management, School of Medicine, Kangwon National University, 510 School of Medicine Building #1 (N414), 1, Kangwondaehak-gil, Chuncheon, Gangwon-do, 24341, Republic of Korea, 82 332508910.ORCID 0000-0003-0245-3583

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cardiovascular DiseasesCerebrovascular DisordersCOVID-19EmotionsFemaleHumansMaleRepublic of KoreaSARS-CoV-2artificial intelligenceAsiacardiovascularcaregiver supportcare modelcerebrovascularcost managementemotionemotional experienceshealth statushospitalizationKoBERTKorean Bidirectional Encoder Representations from Transformersrehabilitationsentiment analysisSouth KoreasurgeryTCMtransformertransitional care modelweb portal

Identifiers

PMID40446313
PMCPMC12143735

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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.