Machine Learning and Statistics in Clinical Research—Bridging the Gap

Karabacak, Mert; Margetis, Konstantinos

doi:10.1001/jamapediatrics.2023.3257

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…When the quantity of obtainable data is limited, it may require multiple attempts with different ML classifiers to identify the optimal ML approach. Compared with traditional statistical models, ML methods can automatically capture the complex relationships in the data, which is more valuable for clinical research [ 30 ], especially in building clinical prediction models [ 31 ]. Six ML-augmented approaches (AdaBoost, Extra Trees, gradient boosting, MLP, SVM, and RF) were employed.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning and Deep EfficientNets in Distinguishing Neonatal Adrenal Hematomas From Neuroblastoma in Enhanced Computed Tomography Images

Xie,

Gong,

Dong

et al. 2024

World J Oncol

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Background The aim of the study was to employ a combination of radiomic indicators based on computed tomography (CT) imaging and machine learning (ML), along with deep learning (DL), to differentiate between adrenal hematoma and adrenal neuroblastoma in neonates. Methods A total of 76 neonates were included in this retrospective study (40 with neuroblastomas and 36 with adrenal hematomas) who underwent CT and divided into a training group (n = 38) and a testing group (n = 38). The regions of interest (ROIs) were segmented by two radiologists to extract radiomics features using Pyradiomics package. ML classifications were done using support vector machine (SVM), AdaBoost, Extra Trees, gradient boosting, multi-layer perceptron (MLP), and random forest (RF). EfficientNets was employed and classified, based on radiometrics. The area under curve (AUC) of the receiver operating characteristic (ROC) was calculated to assess the performance of each model. Results Among all features, the least absolute shrinkage and selection operator (LASSO) logistic regression selected nine features. These radiomics features were used to construct radiomics model. In the training cohort, the AUCs of SVM, MLP and Extra Trees models were 0.967, 0.969 and 1.000, respectively. The corresponding AUCs of the test cohort were 0.985, 0.971 and 0.958, respectively. In the classification task, the AUC of the DL framework was 0.987. Conclusion ML decision classifiers and DL framework constructed from CT-based radiomics features offered a non-invasive method to differentiate neonatal adrenal hematoma from neuroblastoma and performed better than the clinical experts.

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Section: Discussionmentioning

confidence: 99%