Image 1_Integrative prognostic modeling for stage III lung adenosquamous carcinoma post-tumor resection: machine learning insights and web-based implementation.png by Min Liang (363007)

“…Introduction<p>The prognostic landscape of stage III Lung Adenosquamous Carcinoma (ASC) following primary tumor resection remains underexplored. A thoughtfully developed prognostic model has the potential to guide clinicians in patient counseling and the formulation of effective therapeutic strategies.…”

Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx by Changli He (22424818)

“…</p>Results<p>Combining different imputation, sampling, and feature selection strategies yielded 27 datasets, which were trained across nine algorithms to generate 243 models. The optimal model—Simp-SMOTE_rf_GBM, constructed using random forest imputation, SMOTE oversampling, and gradient boosting machine—achieved the highest predictive performance (AUC = 0.954). …”

DataSheet_2_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…</p>Methods<p>The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU.…”

DataSheet_1_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…</p>Methods<p>The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU.…”

DataSheet_1_A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn... by Gaosen Zhang (539619)

Supplementary file 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.xlsx by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”

Image 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”

Supplementary file 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.docx by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”

Image 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…Questionnaires with >20% missing data were excluded. Mean substitution was applied for primary missing data imputation, with multiple imputation by chained equations (MICE) used for sensitivity analysis. …”