Proportion of Feature Importance. by Chunliang Jiang (22477803)

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Identification results of four algorithms on test sets for different feature subsets. by Chunliang Jiang (22477803)

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Definitions of mortality prediction for patients with hypothermia. by Chunliang Jiang (22477803)

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Trends for average AUC for (A) different prediction window and (B) different observation window for each model. by Chunliang Jiang (22477803)

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Metrics used to evaluate the prediction performance. by Chunliang Jiang (22477803)

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Feature selection results for (A) Random Forest, (B) Logistic Regression, (C) XGBoost, and (D) Naïve Bayes. by Chunliang Jiang (22477803)

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Baseline characteristics of included patients. by Chunliang Jiang (22477803)

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Partitioning into training and testing sets. by Chunliang Jiang (22477803)

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Process for selection of the study cohort. by Chunliang Jiang (22477803)

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Appendix DATA: Patient dataset. by Chunliang Jiang (22477803)

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Trends in AUC values for different observation windows and prediction windows for (A) Random Forest, (B) Logistic Regression, (C) XGBoost, and (D) Naïve Bayes four machine learning... by Chunliang Jiang (22477803)

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Non-invasive physiological parameters included in the study. by Chunliang Jiang (22477803)

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Overall process of mortality prediction in patients with hypothermia. by Chunliang Jiang (22477803)

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Data_Sheet_1_A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images.docx by Zhang Yu (5520383)

“…One dermatology graduate student and two general practitioners significantly improved their diagnostic performance, where their AUC values increased from 0.600, 0.537, and 0.575 to 0.849, 0.778, and 0.788, respectively, and their diagnosis consistency was also improved as the kappa values went from 0.191, 0.071, and 0.143 to 0.679, 0.550, and 0.568, respectively. …”

Presentation_1_Differences in tissue-associated bacteria between metastatic and non-metastatic colorectal cancer.PPTX by Peng Zhou (116747)

“…The ROC curves of the selected bacteria showed area under the curve (AUC) values ranging from 0.598 to 0.69; when CEA and the selected bacteria were combined, the AUC values increased from 0.678 to 0.705. In addition, the bacterial composition of tumor-adjacent tissues from the metastatic and non-metastatic CRC groups were also different, and the differential bacteria were consistent with those between metastatic and non-metastatic CRC tumor tissues.…”

Table_1_A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer.docx by Jianyuan Zhou (11485470)

“…When clinical variables and radiomics signature were combined, the complex model showed better performance in predicting TMIT-I tumors, with the AUC values increased to 0.838 [95% CI (0.731–0.914)] in the training set and 0.811 [95% CI (0.634–0.927)] in the validation set.…”

Data_Sheet_1_Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data.docx by Huiquan Wang (13762)

“…The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. …”

The optimal size of the reference cohort for calculating the network impact of a single sample. by Shir Ezra (18699696)

“…The AUC value for each pair of distributions is reported beneath. a) For Δ<i>S</i>, the AUC values are the highest for <i>m</i> = 50 and <i>m</i> = 100. b) For Δ<i>W</i>, the AUC values are the highest for <i>m</i> = 50 and <i>m</i> = 100. c) For <i>θ</i>, the AUC values increase for larger <i>m</i>, and for <i>m</i> ≥ 50 they are larger compared with those of the other network impact parameters.…”

Table_4_Prioritizing key synergistic circulating microRNAs for the early diagnosis of biliary tract cancer.xls by Fei Su (106529)

“…In the internal feature evaluation dataset, the area under the receiver operating characteristic curve (AUC) for each single miRNA was 0.8413 (hsa-let-7c-5p), 0.7143 (hsa-miR-16-5p), 0.8571 (hsa-miR-17-5p), and 0.9365 (hsa-miR-26a-5p), respectively, whereas the synergistic AUC value increased to 1.0000. In the internal test dataset, the single AUC was 0.6500, 0.5125, 0.6750, and 0.7500, whereas the synergistic AUC increased to 0.8375. …”

Table_6_Prioritizing key synergistic circulating microRNAs for the early diagnosis of biliary tract cancer.xls by Fei Su (106529)

“…In the internal feature evaluation dataset, the area under the receiver operating characteristic curve (AUC) for each single miRNA was 0.8413 (hsa-let-7c-5p), 0.7143 (hsa-miR-16-5p), 0.8571 (hsa-miR-17-5p), and 0.9365 (hsa-miR-26a-5p), respectively, whereas the synergistic AUC value increased to 1.0000. In the internal test dataset, the single AUC was 0.6500, 0.5125, 0.6750, and 0.7500, whereas the synergistic AUC increased to 0.8375. …”