Structure of the Kuhn-Munkres Algorithm. by Qingnan Ji (22662198)

“…An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. …”

Data Sheet 1_Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms.zip by Weicheng Pan (12629762)

Data (3). by Qingnan Ji (22662198)

“…An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. …”

Data Sheet 1_Multi-omics analysis and metastasis risk factor prediction in N1b stage PTMC: insights into immune infiltration and therapeutic implications.docx by Hao Dai (4277254)

Curve of data size vs. running time. by Qingnan Ji (22662198)

“…An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. …”

Dataset. by James M. Smoliga (9074225)

“…Linear mixed effects models were used to determine how Swift’s presence or absence in Swift-era games influence Kelce’s performance, relative to historical data. …”

Statistical Analysis Code. by James M. Smoliga (9074225)

“…Linear mixed effects models were used to determine how Swift’s presence or absence in Swift-era games influence Kelce’s performance, relative to historical data. …”

Hyperparameter settings. by Qingnan Ji (22662198)

“…An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. …”

Initial weight values and correlation thresholds. by Qingnan Ji (22662198)

“…An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. …”

Ablation experiment results comparison. by Qingnan Ji (22662198)

“…An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. …”

Adjustment step size. by Qingnan Ji (22662198)

“…An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. …”

SHAP Analysis. by Sazzli Kasim (11218558)

“…</p><p>Methods</p><p>Utilizing data from the REDISCOVER Registry (5,688 participants from 2007 to 2017), 30 clinically relevant features were selected, and several ML algorithms were trained: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Neural Network (NN) and Naive Bayes (NB). …”

Study flow diagram. by Shahrokh Mousavi (21989418)

“…<div><p>Background</p><p>Angina pectoris, a comparatively common complaint among older adults, is a critical warning sign of underlying coronary heart disease. We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.…”

Data Sheet 1_Development and validation of an individualized nomogram for predicting distant metastases in gastric cancer using a CT radiomics-clinical model.docx by Hui-Bin Xue (20356017)

Workflow summary: GRN motif, cell signaling model, and inference framework. by Michael Alexander Ramirez Sierra (20209354)

Data Sheet 1_Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment.docx by Guo Hong (1689283)

“…A total of 38 clinically relevant variables were collected, including demographic data, medical history, cognitive scale scores, and gait data captured by wearable sensors. …”

Data Sheet 1_Integration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery.docx by Zishan Li (15853685)

Data Sheet 1_Machine learning–based risk stratification for gastrointestinal bleeding in ICU patients with cirrhosis: evidence from the MIMIC database.docx by Yuxin Duan (10963881)

“…Key predictive variables were identified through a combination of the Boruta algorithm, correlation analysis, and variance inflation factor (VIF) assessment, ensuring both predictive relevance and control of multicollinearity. …”

Patient Demographics. by Yiye Zhang (9028589)

“…<div><p>Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. …”

Characteristics of RVA visits. by Yiye Zhang (9028589)

“…<div><p>Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. …”