Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".