Nomograms

Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning.

Frontiers in immunology 40191191

BACKGROUND: Recent studies have suggested a potential association between gastric cancer (GC) and myocardial infarction (MI), with shared pathogenic factors. This study aimed to identify these common factors and potential pharmacologic targets.

Gene Expression Profiling Genetic Predisposition to Disease Genome-Wide Association Study Humans Machine Learning Mendelian Randomization Analysis Myocardial Infarction Nomograms Stomach Neoplasms Transcriptome

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Development and validation of machine learning models for early diagnosis and prognosis of lung adenocarcinoma using miRNA expression profiles.

Cancer biomarkers : section A of Disease markers 40171815

ObjectiveStudy aims to develop diagnostic and prognostic models for lung adenocarcinoma (LUAD) using Machine learning（ML）algorithms, aiming to enhance clinical decision-making accuracy.MethodsData from The Cancer Genome Atlas (TCGA) for LUAD patients...

Adenocarcinoma of Lung Aged Biomarkers, Tumor Early Detection of Cancer Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Lung Neoplasms Machine Learning Male MicroRNAs Middle Aged Nomograms Prognosis ROC Curve

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A machine learning model based on placental magnetic resonance imaging and clinical factors to predict fetal growth restriction.

BMC pregnancy and childbirth 40114121

OBJECTIVES: To create a placental radiomics-clinical machine learning model to predict FGR.

Adult Case-Control Studies Female Fetal Growth Retardation Gestational Age Humans Machine Learning Magnetic Resonance Imaging Nomograms Placenta Predictive Value of Tests Pregnancy Retrospective Studies

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Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Scientific reports 40113952

Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and quality of life. Effective tools for assessing the prognosis of these patients are urgently needed to en...

Aged Female Humans Lung Neoplasms Machine Learning Male Middle Aged Nomograms Pleural Effusion, Malignant Prognosis Retrospective Studies

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Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.

BMC cardiovascular disorders 40108540

BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), and to visualize the model results in order to...

Aged Databases, Factual Decision Support Techniques Female Hospital Mortality Humans Machine Learning Male Middle Aged Myocardial Infarction Nomograms Predictive Value of Tests Prognosis Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors Shock, Cardiogenic Time Factors

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Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.

BMC medical imaging 40108506

OBJECTIVE: The purpose of the current study is to explore the value of a nomogram that integrates clinical factors and MRI white matter hyperintensities (WMH) radiomics features in predicting the prognosis at 90 days for patients with acute ischemic ...

Aged Aged, 80 and over Female Humans Ischemic Stroke Machine Learning Magnetic Resonance Imaging Male Middle Aged Nomograms Prognosis Radiomics Retrospective Studies Risk Factors White Matter

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Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.

BMC cancer 40205350

BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its metabolism are closely related to human cancer, but little is known about how lysine metabolism-related ...

Biomarkers, Tumor Carcinogenesis Cell Line, Tumor Cell Proliferation Computational Biology Gene Expression Regulation, Neoplastic Gene Regulatory Networks Humans Lysine Machine Learning Nomograms Prognosis RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis Stomach Neoplasms Vimentin

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Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.

Scientific reports 40204902

To explore the value of applying the MRI-based radiomic nomogram for predicting lymph node metastasis (LNM) in rectal cancer (RC). This retrospective analysis used data from 430 patients with RC from two medical centers. The patients were categorized...

Adult Aged Algorithms Deep Learning Female Humans Lymph Nodes Lymphatic Metastasis Magnetic Resonance Imaging Male Middle Aged Nomograms Radiomics Rectal Neoplasms Retrospective Studies ROC Curve

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Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.

Annals of medicine 40193241

BACKGROUND: Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.

Aged Cardiovascular Diseases Female Humans Logistic Models Lung Neoplasms Machine Learning Male Middle Aged Nomograms Pneumonectomy Postoperative Complications Retrospective Studies Risk Factors ROC Curve

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Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.

Scientific reports 40188298

The burden of diabetic foot ulcers (DFU) is exacerbated in diabetic patients with concomitant arteriosclerotic occlusion disease (ASO) in the lower extremities, who experience more severe symptoms and poorer prognoses. The study aims to develop a pre...

Aged Algorithms Arterial Occlusive Diseases Arteriosclerosis Diabetes Mellitus, Type 2 Diabetic Foot Female Humans Lower Extremity Machine Learning Male Middle Aged Nomograms ROC Curve

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning.

Development and validation of machine learning models for early diagnosis and prognosis of lung adenocarcinoma using miRNA expression profiles.

A machine learning model based on placental magnetic resonance imaging and clinical factors to predict fetal growth restriction.

Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.

Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.

Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.

Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer.

Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.

Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.

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