AIMC Topic: ROC Curve

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Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

Journal of cancer research and therapeutics
BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) oncogene status and predict targeted therapy response in colorectal cancer liver metastases (CRLM).

High-Granularity Machine Learning Prediction of Acute Brain Injury in Patients Receiving Venoarterial Extracorporeal Membrane Oxygenation.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
Acute brain injury (ABI) is prevalent among patients undergoing venoarterial extracorporeal membrane oxygenation (VA-ECMO) and significantly impact recovery. Early prediction of ABI could enable timely interventions to prevent adverse outcomes, but e...

Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.

Scientific reports
Anoikis and immune cell infiltration are pivotal factors in the pathophysiological mechanism of diabetic nephropathy (DN), yet a comprehensive understanding of the mechanism is lacking. This work aimed to pinpoint distinctive anoikis-related genes (A...

Functional blepharoptosis screening with generative augmented deep learning from external ocular photography.

Orbit (Amsterdam, Netherlands)
PURPOSE: To develop and validate a deep learning model for the detection of functional blepharoptosis from external ocular photographs, and to quantify the impact of augmenting the training data with synthetic images on model performance.

An artificial intelligence malnutrition screening tool based on electronic medical records.

Clinical nutrition ESPEN
BACKGROUND & AIMS: Nutrition screening is a fundamental step to ensure appropriate intervention in patients with malnutrition. An automatic tool of nutritional risk screening based on electronic health records will improve efficiency and elevate the ...

Machine Learning-Driven Identification of Hematological and Immunological Biomarkers for Predicting Proliferative Diabetic Retinopathy Progression.

Current eye research
PURPOSE: Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes characterized by neovascularization and retinal detachment, leading to significant vision loss. This study investigates the predictive power of hematological and i...

Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.

BMC gastroenterology
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...

Risk for ocular hypertension progression to early glaucoma: A predictive model and key predictors.

Photodiagnosis and photodynamic therapy
BACKGROUND: Ocular hypertension (OHT) is the most significant risk factor for glaucoma. We aimed to develop a model for predicting OHT progression to early glaucoma and to identify key predictors.

Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis.

BMJ open
OBJECTIVE: In-hospital cardiac arrest (IHCA) presents a critical challenge with low survival rates and limited prediction tools. Despite advances in resuscitation, predicting 30-day survival remains difficult, and current methods lack interpretabilit...

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US.

EBioMedicine
BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these risks post-pandemic have been absent. We aimed to use large electronic health records (EHR) and ML a...