Image 3_Digital pathology-based artificial intelligence model to predict microsatellite instability in gastroesophageal junction adenocarcinomas.png by Zhenqian Li (22004453)

“…A whole-slide image (WSI)-level AI model was constructed by integrating deep learning- generated pathological features with six machine learning algorithms.…”

Supplementary file 1_Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registers.docx by Toni Mora (8145624)

“…</p>Objective<p>To develop and evaluate explainable machine learning models that predict the risk of anaphylaxis using routinely collected clinical data.…”

Data Sheet 1_Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma.xlsx by Bin Zhu (182882)

Data Sheet 2_Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma.xlsx by Bin Zhu (182882)

Data Sheet 3_Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma.xlsx by Bin Zhu (182882)

Comparison of Student Engagement and Exam Success at Introductory Computer Programming Classes on Faculty of Electrical Engineering University of Ljubljana in Years 2023 and 2024 a... by Žiga Rojec (22175137)

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. Meanwhile, the prediction performances of different radiomics models (Tumor, External2, External5, Tumor+External2, Tumor+External5) based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared. …”

An overview of PHyPO. by Shehr Bano (20421706)

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Covariate balance between the subsidised JKN and the uninsured populations. by Vishalie Shah (7301909)

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Estimated coefficients from the Best Linear Projection (BLP) of on . by Vishalie Shah (7301909)

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S8 Data - by Shehr Bano (20421706)

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S6 Data - by Shehr Bano (20421706)

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Results from heterogeneity test using difference-in-means estimator. by Vishalie Shah (7301909)

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Tuning of hyper-parameters in each iteration for AMO. by Shehr Bano (20421706)

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S5 Data - by Shehr Bano (20421706)

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Performance evaluation of different schemes for time-insensitive tasks. by Shehr Bano (20421706)

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S17 Data - by Shehr Bano (20421706)

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S11 Data - by Shehr Bano (20421706)

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Estimated cross-validated loss and standardised coefficients from fitted super learners. by Vishalie Shah (7301909)

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Estimated GATEs for theory-driven effect modifiers. by Vishalie Shah (7301909)

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