Decoding green space supply-demand mismatch through urban morphology: Toward equitable urban planning with explainable machine learning [0.03%]
Lijuan Sun,Wei Liu,Qiqi Liu
Lijuan Sun
An explainable machine learning model (XGBoost-SHAP) was further applied to identify the urban morphological drivers of these mismatches.
Developing a practical machine learning model to predict post implantation syndrome after endovascular aneurysm repair [0.03%]
Jinhua Zhang,Dong Yang,Lei Zhang
Jinhua Zhang
Background: Post-implantation syndrome (PIS) is recognized as a systemic inflammatory response following endovascular aneurysm repair (EVAR), characterized by a high frequency of occurrence and the capacity to provoke car...
Development and Validation of a Machine Learning Model for Predicting 3-Year Overall Survival After Transjugular Intrahepatic Portosystemic Shunt: A Retrospective Multicenter Study [0.03%]
Wenhui Li,Yi Xiang,Guo Han et al.
Wenhui Li et al.
Background and aims: Predicting overall survival (OS) in cirrhotic patients undergoing transjugular intrahepatic portosystemic shunt (TIPS) remains challenging due to the complex interdependencies of clinical variables. T...
A Machine Learning-Based Clinical Tool for Predicting Inadequate Bowel Preparation: Development and Validation [0.03%]
Haotian Chen,Mingyue Xue,Jinxin Shi et al.
Haotian Chen et al.
This study aimed to develop and validate a machine learning model utilizing non-pharmacological parameters to predict the risk of inadequate bowel preparation and develop a clinical risk assessment tool.
Development and Validation of a Machine Learning Model for Incident Heart Failure Prediction in Chronic Kidney Disease: A Multicenter Cohort Study [0.03%]
Yi Lu,Junzhe Chen,Shiyu Zhou et al.
Yi Lu et al.
Background: Chronic kidney disease (CKD) and heart failure (HF) share pathophysiological mechanisms, rendering HF one of the most burdensome cardiovascular complication in CKD. Current HF prediction models, derived from t...
Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time [0.03%]
Gaku Fujiwara,Yoshinari Nagakane,Nobukuni Murakami et al.
Gaku Fujiwara et al.
Conclusions: A simple machine learning model using only prothrombin time-international normalized ratio and activated partial thromboplastin time classified vitamin K antagonist and direct oral anticoagulant exposure with high accuracy and practical clinical utility.
Development and multicenter validation of a predictive model for malignant pleural effusion recurrence [0.03%]
Xin Hu,Yongjie Jiang,Yiluo Heibi et al.
Xin Hu et al.
This study developed and validated a machine learning model to estimate the 3-month recurrence risk of MPE in patients with newly diagnosed lung cancer.
Epidemiology of Risk Stratification, Machine Learning Early Prediction Model, and Tumor Suppressive Mechanism of RHBDF2 in Esophageal Cancer in Gansu Province [0.03%]
Duojie Zhu,Yinggang Che,Huijuan Cheng et al.
Duojie Zhu et al.
A random forest machine learning model exhibited excellent predictive performance (AUC = 0.995) and identified key predictive factors for esophageal cancer.
Unraveling the diagnostic and prognostic signatures of oral microbiota in head and neck cancer [0.03%]
Hojun Sung,Dong-Wook Hyun,Tae Woong Whon et al.
Hojun Sung et al.
A machine learning model successfully differentiated HNSCC patients from controls with an area under the curve of 0.902.
Supervised machine learning model to predict total knee replacement in a large osteoartrhitis real-world evidence dataset using retrospectively 20-year insurance data from Israel [0.03%]
D Demanse,F Saxer,S Gazit et al.
D Demanse et al.
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