Patient baseline information. by Chan Xu (1510759)

“…<div><p>Objective</p><p>The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms.…”

Univariate and multivariate logistic regression. by Chan Xu (1510759)

“…<div><p>Objective</p><p>The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms.…”

Comparison of six machine learning methods. by Chan Xu (1510759)

“…<div><p>Objective</p><p>The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms.…”

Univariate and multivariate Cox regression. by Chan Xu (1510759)

“…<div><p>Objective</p><p>The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms.…”

Table 1_Explainable machine learning-based prediction of early and mid-term postoperative complications in adolescent tibial fractures.docx by Yufeng Wang (274657)

“…Background<p>Adolescent tibial fractures commonly lead to postoperative complications. Conventional coagulation markers (PT/APTT/FIB) lack combinatorial risk assessment. …”

Table_1_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.XLSX by Qin-Yu Zhao (10014626)

Table_2_Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.DOCX by Qin-Yu Zhao (10014626)

Table_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf by Jenish Maharjan (11998331)

“…Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”

Image_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf by Jenish Maharjan (11998331)

“…Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”

Table_1_Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.docx by Liyin Zhang (6371999)

Table 1_Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study.docx by Britta Steffens (13231326)

“…As such, prediction tools utilizing this algorithm are expected to facilitate and safely optimize jaundice risk assessment at hospital discharge with the potential to reduce jaundice-related rehospitalizations.…”

Image_4_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). …”

Image_5_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). …”

Image_3_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). …”

Image_1_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). …”

DataSheet_1_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal chola... by Di Wang (329735)

“…Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). …”

Image_2_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). …”

Data_Sheet_1_A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration ana... by Zuguang Chen (19743121)

Image_3_Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017–2018.TIF by Fang Xia (146439)

Image_2_Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017–2018.TIF by Fang Xia (146439)