<b>External attribution to economic inequality increases algorithm preference</b> by Xuyao Wu (17257993)

“…Future research should attend to potential backfire effects of algorithmic interventions, particularly the risk of undermining individuals’perceptions of fairness in unequal contexts.…”

Table 1_The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.docx by Zhou Liu (1506679)

“…AUGIB characterized by hemorrhagic shock, hypotension, multiple organ dysfunction (MODS), and even circulatory failure is life-threatening. …”

Data Sheet 1_Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a robotic platform.pdf by Jan Benedict Spannenkrebs (20595722)

“…As a target product the readily measurable green fluorescent reporter protein (GFP) is produced over multiple, consecutive iterations of testing. An evaluation of chosen (learning) algorithms for single and dual factor optimization was performed. …”

Data Sheet 1_An individualized risk prediction tool for ectopic pregnancy within the first 10 weeks of gestation based on machine learning algorithms.docx by Xin Du (208780)

“…</p>Conclusion<p>This study employed the CatBoost algorithm to develop an individualized risk prediction model by integrating multiple features from the initial visit. …”

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Early adolescence is an important developmental stage for the increase in prevalence of internalizing disorders and understanding specific factors that predict their onset may be germane to intervention and prevention strategies.</p>Methods<p>We analyzed ~6,000 candidate predictors from multiple knowledge domains (cognitive, psychosocial, neural, biological) contributed by children of late elementary school age (9–10 yrs) and their parents in the ABCD cohort to construct individual-level models predicting the later (11–12 yrs) onset of depression, anxiety and somatic symptom disorder using deep learning with artificial neural networks. …”

Supplementary file 1_Utilizing nutrition-related biomarkers to develop a nutrition-related aging clock for the chinese demographic.docx by Ya-Qing Ma (10010321)

“…The proposed model serves as a reliable tool for predicting biological age and offers a scientific basis for future research on aging mechanisms and personalized interventions.…”

Vaccines, Public Health, and the Politics of Immunity: Safety, Debate, and Global Perspectives by Minehli Arakelians Gheshlagh (21983870)

“…</li><li><b>Case Studies:</b><br>Measles, COVID-19, and emerging viruses are analyzed comparatively, integrating epidemiological data, vaccine rollout strategies, public behavior, and policy interventions. Visual representations—timelines, tables, and network diagrams—are proposed to clarify disease dynamics, intervention strategies, and lessons learned for future preparedness.…”

Table 2_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The majority of the studies employed K-nearest neighbor or Multiple Imputation by Chained Equations for handling missing values and utilized Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta's algorithm for feature selection. …”

Table 4_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The majority of the studies employed K-nearest neighbor or Multiple Imputation by Chained Equations for handling missing values and utilized Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta's algorithm for feature selection. …”

Table 3_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The majority of the studies employed K-nearest neighbor or Multiple Imputation by Chained Equations for handling missing values and utilized Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta's algorithm for feature selection. …”

Table 1_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx by Xu Li (23864)

“…The majority of the studies employed K-nearest neighbor or Multiple Imputation by Chained Equations for handling missing values and utilized Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta's algorithm for feature selection. …”

Data Sheet 1_Establishment and evaluation of a model for clinical feature selection and prediction in gout patients with cardiovascular diseases: a retrospective cohort study.zip by Bingbing Fan (1438732)

“…Multiple ML algorithms—including Decision Tree Learner, LightGBM Learner, K Nearest Neighbors Learner, CatBoost Learner, Gradient Boosting Desicion Tree Learner—were implemented to construct predictive models. …”

Data Sheet 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.pdf by Tim Unger (20457145)

“…Each participant performed multiple repetitions of the drinking task while being recorded by multiple cameras and an optical motion capture system (OMC) for kinematic ground truth. …”

Image 5_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Each participant performed multiple repetitions of the drinking task while being recorded by multiple cameras and an optical motion capture system (OMC) for kinematic ground truth. …”

Image 4_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Each participant performed multiple repetitions of the drinking task while being recorded by multiple cameras and an optical motion capture system (OMC) for kinematic ground truth. …”

Image 2_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Each participant performed multiple repetitions of the drinking task while being recorded by multiple cameras and an optical motion capture system (OMC) for kinematic ground truth. …”

Image 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Each participant performed multiple repetitions of the drinking task while being recorded by multiple cameras and an optical motion capture system (OMC) for kinematic ground truth. …”

Image 3_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Each participant performed multiple repetitions of the drinking task while being recorded by multiple cameras and an optical motion capture system (OMC) for kinematic ground truth. …”

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

“…This study aimed to develop and validate a machine learning model to enhance risk prediction of renal function decline in CKD patients, enabling early and personalized interventions.</p>Methods<p>We developed an ensemble machine learning model using Random Forest, XGBoost, and LightGBM algorithms, incorporating advanced feature selection and hyperparameter tuning. …”