ArticleFrontiers in medicine2025
Prognostic assessment and intelligent prediction system for breast reduction surgery using improved swarm intelligence optimization.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to enhance the accuracy of prognosis assessment for reduction mammaplasty by improving a swarm intelligence optimization algorithm and to develop an intelligent prediction system to support clinical decision-making. Methods: This study enrolled 224 patients who underwent reduction mammaplasty at Xijing Hospital between January 14, 2018, and February 4, 2023, and 137 patients who underwent the same procedure at Plastic Surgery Hospital between January 14, 2018, and May 1, 2020, constituting the training set. Ninety-two patients who underwent reduction mammaplasty at Plastic Surgery Hospital between May 2, 2020, and February 4, 2023, were defined as the test set. Data collection encompassed preoperative anatomical parameters, intraoperative procedural characteristics, and postoperative follow-up outcomes. Prognostic indicators included postoperative complications and the BRQS score. Guided by the Improved Secretary Bird Optimization Algorithm (ISBOA), the optimization algorithm was integrated with an AutoML framework to achieve fully automated optimization spanning from feature selection to model parameter configuration. A classification model was employed to predict the occurrence of postoperative complications, while a regression model was used to predict patient satisfaction at 1 year postoperatively. Results: The ISBOA algorithm significantly outperformed other algorithms in stability, convergence speed, and avoidance of local optima. The AutoML framework achieved an ROC-AUC of 0.9369 and a PR-AUC of 0.8856 for complication prediction (test set), and an R Conclusion: This study successfully constructed a prognosis assessment and intelligent prediction system for reduction mammaplasty based on an improved swarm intelligence optimization algorithm. The results indicate that the ISBOA algorithm exhibits significant advantages in global optimization performance and convergence efficiency. The AutoML model demonstrated excellent performance in predicting complications and assessing quality of life, with its clinical utility further validated by DCA. The developed clinical decision support system provides physicians with a convenient decision-making tool, promising to enhance the scientific rigor and efficiency of medical decision-making and offering a substantial opportunity for improving prognosis quality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.