Image 1_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.tif by Dongdong Zhang (644246)

“…The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.…”

Table 2_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.…”

Table 5_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.…”

Table 3_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.…”

Table 1_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.doc by Dongdong Zhang (644246)

“…The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.…”

Table 4_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.…”

Table 6_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…The ultimate goal was to optimize patient safety and provide evidence-based guidance for the appropriate use of this drug.…”

Data Sheet 1_Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis.zip by Tengfei Zhou (1760092)

“…A total of 29 predictive models were developed, comprising 10 optimal models, primarily utilizing logistic regression or machine learning algorithms. …”

Table 1_Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest.... by Qiang Wu (31071)

“…Objective<p>In this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.…”

Data Sheet 1_Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy.pdf by Xiaote Zhang (21570542)

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.</p>Objective<p>This study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.…”

Table 3_Machine learning-based high-specificity diagnostic model for Talaromyces marneffei infection in febrile patients using routine clinical laboratory data.xlsx by Yingjun Xiao (17603738)

“…Six classic machine learning algorithms were utilized to develop the optimal predictive model through 1000 bootstrap resamplings. …”

Table 2_Machine learning-based high-specificity diagnostic model for Talaromyces marneffei infection in febrile patients using routine clinical laboratory data.xlsx by Yingjun Xiao (17603738)

“…Six classic machine learning algorithms were utilized to develop the optimal predictive model through 1000 bootstrap resamplings. …”

Image 1_Machine learning-based personalized risk prediction model for breast cancer-related lymphedema after surgery.png by Xuemei Peng (2493622)

“…Objective<p>Breast cancer-related lymphedema (BCRL) is a common postoperative complication that significantly impairs patients’ quality of life. …”

Table 1_Machine learning-based personalized risk prediction model for breast cancer-related lymphedema after surgery.xlsx by Xuemei Peng (2493622)

“…Objective<p>Breast cancer-related lymphedema (BCRL) is a common postoperative complication that significantly impairs patients’ quality of life. …”

Table 1_Machine learning-based high-specificity diagnostic model for Talaromyces marneffei infection in febrile patients using routine clinical laboratory data.xlsx by Yingjun Xiao (17603738)

“…Six classic machine learning algorithms were utilized to develop the optimal predictive model through 1000 bootstrap resamplings. …”

Data Sheet 1_Machine learning-based high-specificity diagnostic model for Talaromyces marneffei infection in febrile patients using routine clinical laboratory data.pdf by Yingjun Xiao (17603738)

“…Six classic machine learning algorithms were utilized to develop the optimal predictive model through 1000 bootstrap resamplings. …”

Table 1_High-resolution vessel wall imaging-driven radiomic analysis for the precision prediction of intracranial aneurysm rupture risk: a promising approach.docx by Wenqing Yuan (10896337)

“…Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the training cohort features to identify optimal rupture-associated features. The Rad-score model was constructed by calculating the total score derived from the weighted sum of optimal radiomic features, and three ML models were built using the XGBoost, LightGBM, and CART algorithms, and evaluated using both the test and external validation cohorts.…”

Data Sheet 1_Interpretable machine-learning-based prediction of postpartum haemorrhage in normal vaginal births in Shanghai, China.pdf by Xiao Yao (135696)

“…Five predictive models were constructed using machine-learning algorithms, and these models were subsequently validated and evaluated for performance. …”

Table 1_Risk prediction for gastrointestinal bleeding in pediatric Henoch-Schönlein purpura using an interpretable transformer model.doc by Gahao Chen (21688843)

“…Five machine learning algorithms (Random Forest, XGBoost, LightGBM, CatBoost, and TabPFN-V2) were optimized through nested cross-validation. …”

System cost calculation. by Syed Zahurul Islam (9635163)

“…The objectives of this project are to design and optimize the PV-powered irrigation system and implement an Arduino-enabled automatic system with SMS-triggered functionality. …”