دورية أكاديمية
Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification
| العنوان: | Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification |
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| المؤلفون: | Sadegh-Zadeh, Seyed-Ali, Sakha, Hanie, Movahedi, Sobhan, Fasihi Harandi, Aniseh, Ghaffari, Samad, Javanshir, Elnaz, Ali, Syed Ahsan, Hooshanginezhad, Zahra, Hajizadeh, Reza |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2023 |
| المجموعة: | Staffordshire University: STORE - Staffordshire Online Repository |
| الوصف: | Background Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients. Objective To devise an ML algorithm for early mortality prediction in PE patients by employing clinical and laboratory variables. Methods This study utilized diverse oversampling techniques to improve the performance of various machine learning models including ANN, SVM, DT, RF, and AdaBoost for early mortality prediction. Appropriate oversampling methods were chosen for each model based on algorithm characteristics and dataset properties. Predictor variables included four lab tests, eight physiological time series indicators, and two general descriptors. Evaluation used metrics like accuracy, F1_score, precision, recall, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves, providing a comprehensive view of models' predictive abilities. Results The findings indicated that the RF model with random oversampling exhibited superior performance among the five models assessed, achieving elevated accuracy and precision alongside high recall for predicting the death class. The oversampling approaches effectively equalized the sample distribution among the classes and enhanced the models' performance. Conclusions The suggested ML technique can efficiently prognosticate mortality in patients afflicted with acute PE. The RF model with random oversampling can aid healthcare professionals in making well-informed decisions regarding the treatment of patients with acute PE. The study underscores the significance of oversampling methods in managing imbalanced data and emphasizes the potential of ML algorithms in refining early mortality prediction for PE patients. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | text |
| اللغة: | English |
| العلاقة: | https://eprints.staffs.ac.uk/8065/1/Advancing%20prognostic%20precision%20in%20pulmonary%20embolism.pdfTest; Sadegh-Zadeh, Seyed-Ali , Sakha, Hanie, Movahedi, Sobhan, Fasihi Harandi, Aniseh, Ghaffari, Samad, Javanshir, Elnaz, Ali, Syed Ahsan, Hooshanginezhad, Zahra and Hajizadeh, Reza (2023) Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification. Computers in Biology and Medicine, 167. p. 107696. ISSN 1879-0534 |
| DOI: | 10.1016/j.compbiomed.2023.107696 |
| الإتاحة: | https://doi.org/10.1016/j.compbiomed.2023.107696Test http://eprints.staffs.ac.uk/8065Test/ https://eprints.staffs.ac.uk/8065/1/Advancing%20prognostic%20precision%20in%20pulmonary%20embolism.pdfTest |
| حقوق: | cc_by |
| رقم الانضمام: | edsbas.D7EECA7F |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.compbiomed.2023.107696 |
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