Cited by Development of machine learning-based models to predict congenital heart disease: A matched case-control study

Prediction Models for Early Detection of Alzheimer: Recent Trends and Future Prospects DOI

Ishleen Kaur,

Rajinder K. Sachdeva

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

Optimizing stability of heart disease prediction across imbalanced learning with interpretable Grow Network DOI

Simon Bin Akter,

Sumya Akter,

Rakibul Hasan

et al.

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: unknown, P. 108702 - 108702

Published: March 1, 2025

Language: Английский

Citations

Screening for Anomalous Safety Condition Among Existing Buildings Using Explainable Machine Learning DOI

Jie Liu, Guiwen Liu, Neng Wang

et al.

Structural Control and Health Monitoring, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

To ensure a safe environment for occupants, evaluating the physical status and service performance of existing buildings is essential. However, large‐scale building condition assessment usually relies on expertise judgment inspectors, which can be costly laborious due to unclear priorities, ambiguous procedures, ineffective operations. address these challenges, this study proposes an explainable machine learning‐based screening model anomalous safety among buildings, narrowing down scope requiring further detailed inspection monitoring. Initially, imbalanced dataset 18,090 survey reports unsafe labels collected. Then, synthetic minority oversampling technique (SMOTE) conducted balance dataset. Subsequently, seven learning models are trained utilizing 10‐fold cross‐validation with grid search. Findings reveal that, based balanced dataset, ensemble significantly better than that individual models. Specifically, XGBoost achieves highest performance, macro‐F1 98.49%, G‐mean value accuracy 98.49%. The final predictive (the SMOTE‐based model) explained using SHapley Additive exPlanations (SHAP). Service year, structure, location three most important features influencing structural safety. This represents promising approach automated optimizing resource allocation, enhancing effectiveness in decision‐making construction maintenance.

Language: Английский

Citations

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice DOI

Mingzhi Lin,

Jiuqi Guo,

Zhilin Gu

et al.

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 2, 2025

The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection the complex physiological pathological changes body from molecular perspective, providing new microscopic insights into research. However, due vast volume complexity data, accurately describing, utilising, translating these biomedical data demands substantial effort. Researchers clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery causal inference using various data. These AI approaches, integrated with research, have shown promising outcomes studies. In this review, we outline integrating machine learning, one most successful applications AI, summarise representative models developed that leverage facilitate exploration underlying mechanisms clinical practice. Particular emphasis is placed on effectiveness extract potential information address current gaps. We discuss challenges opportunities routine diagnostic therapeutic practices anticipate future development novel wider application field diseases.

Language: Английский

Citations

Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study DOI

Jieyang Zhu,

Chenxi Xu,

Yi Jiang

et al.

Orthopaedic Surgery, Journal Year: 2024, Volume and Issue: 16(8), P. 2066 - 2080

Published: July 1, 2024

Objective Total hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore risk factors associated with allogeneic blood transfusion after surgery and develop a dynamic prediction model predict post‐operative requirements. will provide more accurate guidance perioperative humoral management rational allocation of medical resources. Methods We retrospectively analyzed data from 829 patients who underwent total at three third‐class hospitals between January 2017 August 2023. Patient one hospital were used development, whereas other two external validation. Logistic regression analysis was screen characteristic subsets related transfusion. Various machine learning algorithms, including logistic regression, SVA (support vector machine), K‐NN (k‐nearest neighbors), MLP (multilayer perceptron), naive Bayes, decision tree, random forest, gradient boosting, process construct models. A 10‐fold cross‐validation algorithm facilitated comparison predictive performance models, resulting selection best‐performing development an open‐source computing program. Results BMI (body mass index), surgical duration, IBL (intraoperative loss), anticoagulant history, utilization rate tranexamic acid, Pre‐Hb, Pre‐ALB included as well independent factors. The average area under curve (AUC) values each follows: (0.98); (0.91); k‐NN (0.87) MLP, (0.96); Bayes (0.97); tree (0.87); forest boosting (0.97). web calculator based on best is available at: ( https://nomo99.shinyapps.io/dynnomapp/ ). Conclusion Utilizing computer algorithm, high discrimination accuracy (AUC > 0.5) developed. demonstrated superior differentiation reliability, thereby successfully passing model's strong generalizability applicability have significant implications clinicians, aiding identification postoperative

Language: Английский

Citations

Identifying At-Risk Patients for Congenital Heart Disease Using Integrated Predictive Models and Fuzzy Clustering Analysis: A Cross-Sectional Study DOI

Amirreza Salehi, Majid Khedmati

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e39609 - e39609

Published: Oct. 1, 2024

Language: Английский

Citations

Development of machine learning-based models to predict congenital heart disease: A matched case-control study DOI

Shutong Zhang,

Chenxi Kang,

Jing Cui

et al.

International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 195, P. 105741 - 105741

Published: Dec. 2, 2024

Language: Английский

Citations