AIMC Topic: Boosting Machine Learning Algorithms

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MCD-LightGBM System for Intelligent Analyzing Heterogeneous Clinical Drug Therapeutic Effects.

IEEE journal of biomedical and health informatics Jun 1, 2025
Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active and challenging problem. In high-risk decision-making domain such as healthcare, inappropriate trea...
Humans Machine Learning Algorithms Boosting Machine Learning Algorithms Drug Therapy
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Predicting thyroid cancer recurrence using supervised CatBoost: A SHAP-based explainable AI approach.

Medicine May 30, 2025
Recurrence prediction in well-differentiated thyroid cancer remains a clinical challenge, necessitating more accurate and interpretable predictive models. This study investigates the use of a supervised CatBoost classifier to predict recurrence in we...
Neoplasm Recurrence, Local Supervised Machine Learning Boosting Machine Learning Algorithms Humans Risk Assessment Adult Female Male Aged Thyroid Neoplasms Middle Aged ROC Curve
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A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

Scientific reports May 25, 2025
The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality ...
Retrospective Studies Female Aged Machine Learning Prognosis Hospital Mortality ROC Curve Acute Disease Male Middle Aged Neoplasms Humans Pulmonary Embolism Boosting Machine Learning Algorithms
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BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Chemical research in toxicology May 19, 2025
Over the past 60 years, drug-induced liver injury (DILI) has played a key role in the withdrawal of marketed drugs due to safety concerns. Early prediction of DILI is crucial for developing safer pharmaceuticals, yet current and testing methods are...
Machine Learning Pharmaceutical Preparations United States Food and Drug Administration Chemical and Drug Induced Liver Injury Boosting Machine Learning Algorithms Humans
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Predicting hospital outpatient volume using XGBoost: a machine learning approach.

Scientific reports May 16, 2025
Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allo...
Outpatients Boosting Machine Learning Algorithms Algorithms Forecasting Humans Machine Learning
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XGBOrdinal: An XGBoost Extension for Ordinal Data.

Studies in health technology and informatics May 15, 2025
We propose XGBOrdinal, an extension of XGBoost designed for ordinal classification problems commonly found in fields like medicine, where outcomes are often represented as scores, scales, stages, or grades. The proposed approach builds on the theoret...
Machine Learning Humans Algorithms Boosting Machine Learning Algorithms
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Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.

Scientific reports May 15, 2025
Formulas based on red blood cell indices have been used to differentiate between iron deficiency anemia (IDA) and thalassemia (Thal). However, they exhibit varying efficiencies. In this study, we aimed to develop a tool for discriminating between IDA...
Machine Learning Algorithms Female Young Adult Adult Male Humans ROC Curve Anemia, Iron-Deficiency Middle Aged Diagnosis, Differential Erythrocyte Indices Boosting Machine Learning Algorithms Adolescent Random Forest Thalassemia
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Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier.

Computer methods in biomechanics and biomedical engineering May 1, 2025
Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the...
Boosting Machine Learning Algorithms Deep Learning Emotions Humans Natural Language Processing Neural Networks, Computer
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Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

World journal of gastroenterology Mar 14, 2025
BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). Mitochondrial dysfunction impairs oxidative phosphorylation and increases ...
Logistic Models Random Forest Non-alcoholic Fatty Liver Disease Cohort Studies Polymorphism, Single Nucleotide Male Humans DNA, Mitochondrial Middle Aged Adult Diabetes Complications Case-Control Studies Boosting Machine Learning Algorithms
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A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications.

Clinical and translational science Mar 1, 2025
Approaches to artificial intelligence and machine learning (AI/ML) continue to advance in the field of drug development. A sound understanding of the underlying concepts and guiding principles of AI/ML implementation is a prerequisite to identifying ...
Clinical Trials as Topic Boosting Machine Learning Algorithms Algorithms Drug Development Humans Artificial Intelligence Machine Learning
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