Image 1_Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning.pdf by Jianhui Chen (1362585)

“…Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. …”

Distributed Estimation of Principal Support Vector Machines for Sufficient Dimension Reduction by Jun Jin (551362)

“…However, the computational burden of the principal support vector machines method constrains its use for massive data. To address this issue, we propose a naive and a refined distributed estimation algorithms for fast implementation when the sample size is large. …”

Additional file 1 of The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László (21420583)

“…There is provided further information about data collection and processing, machine learning algorithms, and other program codes, and more details about the findings…”

Table 1_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf by Salwa Hassanein (20843468)

“…However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”

Table 1_Risk prediction for cardiovascular events and all-cause mortality in maintenance hemodialysis patients.docx by Mengxia Cao (22500635)

“…Objective<p>This study is designed to develop predictive models for cardiovascular events (CVE) and all-cause mortality in maintenance hemodialysis (MHD) patients using machine learning (ML) algorithms. Furthermore, we aim to compare the performance of these ML-based models with that of traditional Cox regression models.…”

Table 2_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf by Salwa Hassanein (20843468)

“…However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”

Table 3_Artificial intelligence in nursing: an integrative review of clinical and operational impacts.pdf by Salwa Hassanein (20843468)

“…However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”

Database of Electromyography and Digit Force during Precision Grasping of Objects at Different Weights by Salman Khan (18105799)

“…This dataset is collected from 15 healthy participants, with around 50 trials for each participant. This dataset can be used for the development and validation of machine learning algorithms to apply in prosthetics and orthosis.…”

Table 1_Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach.docx by Eun Namgung (4053919)

“…In this PreventIon of CArdiovascular events in iSchemic Stroke patients with high risk of cerebral hemOrrhage for reducing cognitive decline substudy, machine learning on clinical and imaging data was used to predict cognitive decline over 9 months in PSCI patients.…”

Table 1_The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma.docx by Qian Shen (89639)

“…Quantitative radiomics features of the intratumoral and peritumoral regions of 2 mm and 5 mm on CT images were extracted, and radiomics models of Tumor, External2, External5, Tumor+ External2, and Tumor+External5 were constructed and used to train six different machine learning algorithms. …”

Table 1_Correlation between blood urea nitrogen/albumin levels and 30-day all-cause mortality in critically Ill patients with heart failure: a retrospective cohort study and predic... by Wen-Ting Sun (7378373)

“…Nine machine learning (ML) algorithms were used to build predictive models, and, in addition, the Shapley additive interpretation (SHAP) method was used to determine feature importance.…”

Supplementary file 2_Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.xlsx by LiHan Lin (19267964)

“…Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.…”

Supplementary file 3_Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.xlsx by LiHan Lin (19267964)

“…Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.…”

Supplementary file 1_Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models.pdf by LiHan Lin (19267964)

“…Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. Logistic regression analysis was employed to identify key factors influencing falls in these populations.…”

Table 1_Ethical and legal concerns in artificial intelligence applications for the diagnosis and treatment of lung cancer: a scoping review.docx by Ghenwa Chamouni (22425346)

“…</p>Results<p>The most frequently reported ethical concern was data privacy. Other recurrent issues included informed consent, no harm to patients, algorithmic bias and fairness, transparency, equity in AI access and use, and trust. …”

Josefina Barrera Morelli: Lectures on cheMOOmetrics: udderly accurate whey to predict by Josefina Barrera Morelli (14074149)

“…For this purpose, I have evaluated various prepocessing techniques that are applied over the MIR spectra. Subsequently used the preprocessed data is combined with information on detailed composition to develop predictive models with algorithms (mostly machine learning) in my computer, to try to obtain the best accuracies possible. …”

OHID-1: A New Large Hyperspectral Image Dataset for Multi-Classification by Jianwen Deng (19780538)

“…Furthermore, this study demonstrates the utility of OHID-1 by testing it with selected hyperspectral classification algorithms. This dataset will be useful to advance cutting-edge research in urban sustainable development science, land use analysis. …”

Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”

Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…</p>Methods<p>We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. …”