AIMC Topic: ROC Curve

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Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants.

Redox biology Dec 16, 2024
OBJECTIVE: To develop and validate a machine learning model incorporating dietary antioxidants to predict cardiovascular disease (CVD)-cancer comorbidity and to elucidate the role of antioxidants in disease prediction.
ROC Curve Antioxidants Nutrition Surveys Dietary Supplements Male Cardiovascular Diseases Middle Aged Neoplasms Comorbidity Humans Machine Learning Female Aged
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Utilizing machine learning approaches to investigate the relationship between cystatin C and serious complications in esophageal cancer patients after esophagectomy.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer Dec 16, 2024
BACKGROUND: The purpose of this study is to investigate the relationship between preoperative cystatin C levels and the risk of serious postoperative complications in esophageal cancer (EC) patients, utilizing advanced machine learning (ML) methodolo...
Esophagectomy Esophageal Neoplasms ROC Curve Logistic Models Predictive Value of Tests Cystatin C Humans Machine Learning Female Aged Male Middle Aged Postoperative Complications Cohort Studies
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Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method.

Magnetic resonance imaging Dec 14, 2024
PURPOSE: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.
Humans Retrospective Studies Female Aged Adult Receptor, ErbB-2 ROC Curve Multiparametric Magnetic Resonance Imaging Sensitivity and Specificity Contrast Media Breast Neoplasms Magnetic Resonance Imaging Middle Aged Breast Deep Learning Image Interpretation, Computer-Assisted
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Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

Computer methods and programs in biomedicine Dec 13, 2024
BACKGROUND AND OBJECTIVE: Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and periop...
Humans Risk Assessment Female ROC Curve Risk Factors Machine Learning Bayes Theorem Aged Coronary Artery Disease Male Surgical Procedures, Operative Middle Aged
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A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.

Annals of laboratory medicine Dec 13, 2024
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles...
Retrospective Studies Intensive Care Units Female Hospitalization Aged Humans Machine Learning International Classification of Diseases Vital Signs Heart Arrest Aged, 80 and over Adult Male Middle Aged ROC Curve
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Longitudinal Model Shifts of Machine Learning-Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals.

Journal of medical Internet research Dec 13, 2024
BACKGROUND: In recent years, machine learning (ML)-based models have been widely used in clinical domains to predict clinical risk events. However, in production, the performances of such models heavily rely on changes in the system and data. The dyn...
ROC Curve Longitudinal Studies Retrospective Studies Acute Kidney Injury Female Aged Male Middle Aged Risk Assessment Hospitals Sepsis Humans Machine Learning COVID-19
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The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model.

Frontiers in immunology Dec 12, 2024
BACKGROUND: Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characterized by a high clinical morbidity and mortality rate, endangering patients' lives and health. The pur...
Transcriptome Female Computational Biology Sepsis Humans Adult ROC Curve Nomograms Machine Learning Male Gene Regulatory Networks Biomarkers Support Vector Machine Middle Aged Prognosis Databases, Genetic Gene Expression Profiling RNA, Long Noncoding
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Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

European journal of clinical investigation Dec 11, 2024
BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the re...
Humans Machine Learning Ischemic Attack, Transient Thromboembolism Logistic Models Ischemic Stroke Area Under Curve Aged, 80 and over Anticoagulants Male Registries Middle Aged Stroke Risk Assessment Myocardial Infarction ROC Curve Thrombosis Algorithms Administration, Oral Atrial Fibrillation Female Risk Factors Aged
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Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Journal of the American Heart Association Dec 10, 2024
BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage...
Prognosis Retrospective Studies Tomography, X-Ray Computed Female Risk Factors Humans Machine Learning Hospital Mortality ROC Curve Cerebral Hemorrhage Predictive Value of Tests Acute Disease Aged Aged, 80 and over Male Middle Aged Risk Assessment
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Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction.

PloS one Dec 9, 2024
Cone snails are venomous marine gastropods comprising more than 950 species widely distributed across different habitats. Their conical shells are remarkably similar to those of other invertebrates in terms of color, pattern, and size. For these reas...
Supervised Machine Learning Animal Shells Conus Snail Animals Deep Learning ROC Curve Image Processing, Computer-Assisted
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