Judgment matrix of criterion layer U₄. by Yixuan Lu (9987980)

“…Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. …”

The evaluation index system. by Yixuan Lu (9987980)

“…Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. …”

Judgment matrix of criterion layer U₂. by Yixuan Lu (9987980)

“…Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. …”

Score of evaluation level. by Yixuan Lu (9987980)

“…Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. …”

Weights of sample data. by Yixuan Lu (9987980)

“…Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. …”

Sample data of K-means clustering. by Yixuan Lu (9987980)

“…Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. …”

Judgment matrix of criterion layer U₁. by Yixuan Lu (9987980)

“…Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. …”

Data_Sheet_1_Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19.docx by Xiao-Hui Wang (1762348)

Image_1_Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.TIFF by Zirui Meng (10671261)

Image_2_Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.TIFF by Zirui Meng (10671261)

Image_3_Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.TIF by Zirui Meng (10671261)

Supplementary file 1_Machine learning algorithms to predict epidural-related maternal fever: a retrospective study.docx by Xiaohui Guo (131711)

Growth rate comparison between models and experimental results in mono-culture and co-culture. by Elias Vera-Siguenza (16993830)

Table 1_Identification of routine blood derived hematological and lipid indices in ILD through machine learning; a retrospective case-control study.docx by Lichen Zhu (22399222)

“…The random forest model outperformed seven other machine learning approaches, with AUC values of 0.868 (validation set), 0.885 (test set), and 0.849 (external cohort), demonstrating consistent predictive accuracy.</p>Discussion<p>Based on these findings, we developed an online prediction tool to assist primary care clinicians in assessing the risk of ILD in suspected cases. …”

Image 1_Identification of routine blood derived hematological and lipid indices in ILD through machine learning; a retrospective case-control study.tif by Lichen Zhu (22399222)

“…The random forest model outperformed seven other machine learning approaches, with AUC values of 0.868 (validation set), 0.885 (test set), and 0.849 (external cohort), demonstrating consistent predictive accuracy.</p>Discussion<p>Based on these findings, we developed an online prediction tool to assist primary care clinicians in assessing the risk of ILD in suspected cases. …”

Table 2_Hemophagocytic lymphohistiocytosis as the initial manifestation of Epstein–Barr virus-related T/NK-cell lymphoproliferative disorders in a pediatric patient: a case report... by Lianwei Ma (10517346)

“…Developing optimized diagnostic algorithms and evidence-based treatment strategies is essential to improve outcomes of patients.…”

Table 4_Hemophagocytic lymphohistiocytosis as the initial manifestation of Epstein–Barr virus-related T/NK-cell lymphoproliferative disorders in a pediatric patient: a case report... by Lianwei Ma (10517346)

“…Developing optimized diagnostic algorithms and evidence-based treatment strategies is essential to improve outcomes of patients.…”

Table 3_Hemophagocytic lymphohistiocytosis as the initial manifestation of Epstein–Barr virus-related T/NK-cell lymphoproliferative disorders in a pediatric patient: a case report... by Lianwei Ma (10517346)

“…Developing optimized diagnostic algorithms and evidence-based treatment strategies is essential to improve outcomes of patients.…”

Table 1_Hemophagocytic lymphohistiocytosis as the initial manifestation of Epstein–Barr virus-related T/NK-cell lymphoproliferative disorders in a pediatric patient: a case report... by Lianwei Ma (10517346)

“…Developing optimized diagnostic algorithms and evidence-based treatment strategies is essential to improve outcomes of patients.…”

Supplementary file 1_Hemophagocytic lymphohistiocytosis as the initial manifestation of Epstein–Barr virus-related T/NK-cell lymphoproliferative disorders in a pediatric patient: a... by Lianwei Ma (10517346)

“…Developing optimized diagnostic algorithms and evidence-based treatment strategies is essential to improve outcomes of patients.…”