Image 1_Development and application of machine learning models for hematological disease diagnosis using routine laboratory parameters: a user-friendly diagnostic platform.jpeg by Jingya Liu (13338460)

“…</p>Methods<p>In this study, we employed 54 clinical and conventional laboratory parameters. By optimally combining multiple feature selection methods and machine learning algorithms, we developed 7 machine learning models with varying feature set sizes. …”

Image 1_Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study.tif by Qing-Lin Ren (22516586)

“…Feature importance was assessed using Shapley Additive Explanations (SHAP). A web-based nomogram was constructed for clinical application.…”

Image 1_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.…”

Image 2_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

“…Propensity score matching (PSM) and machine learning algorithms were used for confounder adjustment and feature selection.…”

Image 1_Safety assessment of temozolomidee: real-world adverse event analysis from the FAERS database.png by Yu Liu (6938)

“…Specific detection algorithms also include report Odds ratio (ROR), Proportional Report ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item Gamma-Poisson constrictor (MGPS).…”

FCP dataset for forecasting temperature, PV, price, and load by Hanwen Zhang (18259666)

“…</p><p dir="ltr">• To design and develop data-driven algorithms for accurate and reliable charging supplydemand forecasting and cost-optimal scheduling with large-volume and high-resolution data.…”

Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx by Fanhai Bu (22315168)

“…LASSO regression selected five predictors: the neutrophil-to-lymphocyte ratio (NLR), admission National Institutes of Health Stroke Scale (NIHSS) score, the Alberta Stroke Program Early CT Score (ASPECTS), atrial fibrillation, and blood glucose. While tree-based methods like XGBoost and LightGBM showed elevated training performance (e.g., XGBoost training AUC = 0.878) but significant drops in validation (AUC = 0.791), LR demonstrated optimal performance: robust training AUC (0.792), minimal validation degradation (AUC = 0.787). …”

GBM-Reservoir: Dataset and Segmentations by Naida Solak (20230698)

“…This dataset can be utilized for various tasks, such as developing fully automated segmentation algorithms for new, unseen brain tumor cases, particularly through deep learning-based approaches, since ground truth is provided for each sample.…”

Table 2_Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study.docx by Xinxin Qin (14280806)

“…We plotted the correlation heat maps of the predictor variables. We selected an optimal model and constructed a web-based online calculator for predicting the risk of distant metastasis of gastric cancer.…”

Table 1_Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study.docx by Xinxin Qin (14280806)

“…We plotted the correlation heat maps of the predictor variables. We selected an optimal model and constructed a web-based online calculator for predicting the risk of distant metastasis of gastric cancer.…”

Table 3_Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study.docx by Xinxin Qin (14280806)

“…We plotted the correlation heat maps of the predictor variables. We selected an optimal model and constructed a web-based online calculator for predicting the risk of distant metastasis of gastric cancer.…”

Massive Mixed Models in Julia by Phillip M. Alday (2814652)

“…Keeping everything in one language makes it much easier for experimentation of potential computational enhancements, such as optimizing the storage of various matrices and intermediate quantities. …”

DataSheet1_Assessing sepsis-induced immunosuppression to predict positive blood cultures.pdf by Enrique Hernández-Jiménez (559505)

“…Although not widely accepted, several clinical and artificial intelligence-based algorithms have been recently developed to predict bacteremia. …”

Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis by Yuhan Zhang (2792089)

“…</p><p>Seven machine learning algorithms were used to construct the prediction model, and RF performed the best.…”

Table 1_Plasma exosomal lncRNA-related signatures define molecular subtypes and predict survival and treatment response in hepatocellular carcinoma.docx by Fangmin Zhong (17415318)

“…Prognostic models were developed and optimized via 10 machine learning algorithms with 10-fold cross-validation. …”

Table 2_An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development... by Landan Kang (20108214)

“…While the LASSO model achieved the highest area under the receiver operating characteristic curve (AUC, 0.802), the NB model demonstrated greater clinical net benefit, higher reclassification performance as measured by the Net Reclassification Improvement (NRI, which evaluates whether patients are more accurately assigned to higher- or lower-risk groups, which reflects the average improvement in distinguishing high-risk from low-risk patients) and Integrated Discrimination Improvement (IDI), and greater robustness in SMOTE-based sensitivity analyses. In the external validation cohort (n = 136), it maintained strong generalization with an AUC of 0.777 (95% confidence interval [CI]: 0.645–0.887), accuracy of 0.801 (95% CI: 0.735–0.860), sensitivity of 0.792, and specificity of 0.804, supporting its selection as the optimal model for this high-risk population.…”

Table 1_An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development... by Landan Kang (20108214)

“…While the LASSO model achieved the highest area under the receiver operating characteristic curve (AUC, 0.802), the NB model demonstrated greater clinical net benefit, higher reclassification performance as measured by the Net Reclassification Improvement (NRI, which evaluates whether patients are more accurately assigned to higher- or lower-risk groups, which reflects the average improvement in distinguishing high-risk from low-risk patients) and Integrated Discrimination Improvement (IDI), and greater robustness in SMOTE-based sensitivity analyses. In the external validation cohort (n = 136), it maintained strong generalization with an AUC of 0.777 (95% confidence interval [CI]: 0.645–0.887), accuracy of 0.801 (95% CI: 0.735–0.860), sensitivity of 0.792, and specificity of 0.804, supporting its selection as the optimal model for this high-risk population.…”

Table 1_CEACAM6 as a machine learning derived immune biomarker for predicting neoadjuvant chemotherapy response in HR+/HER2− breast cancer.xlsx by Dalang Fang (22130155)

“…Overlapping DEGs were further screened using LASSO, random forest, and SVM-RFE algorithms. Predictive models were constructed with 10 machine learning algorithms and interpreted using SHAP. …”

Enhancing the Robustness of Vehicle Re-Identification in Intelligent Transportation Systems by Mei Qiu (21081170)

“…</p><p><br></p><p dir="ltr">We developed a comprehensive data set generation pipeline that uses vehicle detection algorithms with confidence scores to select optimal Regions of Interest (ROI) for image cropping. …”

Data Sheet 1_Resveratrol contributes to NK cell-mediated breast cancer cytotoxicity by upregulating ULBP2 through miR-17-5p downmodulation and activation of MINK1/JNK/c-Jun signali... by Bisha Ding (5803799)

“…This finding identifies RES as a potentially effective therapeutic agent for inhibiting BC progression and optimizing NK cell-based cancer immunotherapy.</p>…”