Scalable Algorithms for Large Competing Risks Data حسب Eric S. Kawaguchi (9581039)

Variable selection by RFE. حسب Xiaoli Shen (2208601)

DataSheet_1_Overexpression of DBT suppresses the aggressiveness of renal clear cell carcinoma and correlates with immune infiltration.zip حسب Chiyu Zhang (119192)

"…<p>Conventional therapy for kidney renal clear cell carcinoma (KIRC) is unpromising. …"

Table 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Image 3_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Image 4_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Table 1_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Image 1_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Table 2_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xls حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Table 4_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.xlsx حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Image 2_Unveiling ammonia-induced cell death: a new frontier in clear cell renal cell carcinoma prognosis.tif حسب Peize Yu (21837977)

"…Background<p>Clear cell renal cell carcinoma (KIRC) is the most aggressive renal carcinoma subtype of renal carcinoma, characterized by high mortality, early metastasis, and resistance to treatment. …"

Table 1_Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.docx حسب Hong Chen (108084)

"…</p>Methods<p>We developed an ensemble machine learning model using Random Forest, XGBoost, and LightGBM algorithms, incorporating advanced feature selection and hyperparameter tuning. …"

Data_Sheet_6_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"

Data_Sheet_1_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"

Data_Sheet_8_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"

Data_Sheet_4_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"

Data_Sheet_7_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"

Data_Sheet_5_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"

Data_Sheet_2_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"

Data_Sheet_3_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV حسب Li Xinsai (13841782)

"…</p>Methods<p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.…"