Data_Sheet_1_Robust optimization for casualty scheduling considering injury deterioration and point-edge mixed failures in early stage of post-earthquake relief.docx by Yufeng Zhou (3702208)

Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx by Changli He (22424818)

“…</p>Conclusion<p>This study established and validated a machine learning model for predicting VTE risk in lymphoma patients, with the optimal model demonstrating excellent discriminatory ability (AUC = 0.954). …”

Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx by Juli Chen (12187358)

“…Furthermore, the nomogram showed good compliance in predicting the risk of TB and a higher net benefit than the “EHR” model for threshold probabilities of 0.2–1.…”

Table 1_An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.doc by Haoran Tao (8466261)

“…Through SHAP analysis, we identified white blood cell counts, lymphocyte counts, and blood urea nitrogen levels as the model’s main predictors.</p>Conclusion<p>The XGBoost algorithm-based prediction model can be used to dynamically estimate the risk of LEA in DFU patients, making it a valuable tool for preventing the progression of DFUs to amputation.…”

S1 Code - by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

S1 Data - by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

Image 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…The distribution by species was: wasps (25.5%), honey bees (8.9%), and unknown species (65.6%). The optimal Extra Trees model achieved an AUC of 0.982, recall of 0.956, and precision of 0.926 in the held-out validation set. …”

Image 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…The distribution by species was: wasps (25.5%), honey bees (8.9%), and unknown species (65.6%). The optimal Extra Trees model achieved an AUC of 0.982, recall of 0.956, and precision of 0.926 in the held-out validation set. …”

The flow chart for the study. by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

ROC curve of six impact indicators. by Gaosha Li (20570760)

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”

Supplementary file 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.xlsx by Feng Han (10919)

“…The distribution by species was: wasps (25.5%), honey bees (8.9%), and unknown species (65.6%). The optimal Extra Trees model achieved an AUC of 0.982, recall of 0.956, and precision of 0.926 in the held-out validation set. …”

Supplementary file 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.docx by Feng Han (10919)

“…The distribution by species was: wasps (25.5%), honey bees (8.9%), and unknown species (65.6%). The optimal Extra Trees model achieved an AUC of 0.982, recall of 0.956, and precision of 0.926 in the held-out validation set. …”

Data_Sheet_1_Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients.docx by Yuan-yuan Guo (13789975)

Results of Comprehensive weighting. by Hao Yang (328526)

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”

VIF analysis results for hazard-causing factors. by Hao Yang (328526)

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”

Benchmark function information. by Hao Yang (328526)

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”

Geographical distribution of the study area. by Hao Yang (328526)

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”

Flow chart of this study. by Hao Yang (328526)

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”

Data_Sheet_1_Adaptive restraint design for a diverse population through machine learning.pdf by Wenbo Sun (531308)

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method by Tao Huang (110613)

“…Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model.…”