AIMC Topic: Nomograms
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Machine-learning based prediction model for acute kidney injury induced by multiple wasp stings.
Toxicon : official journal of the International Society on Toxinology
Sep 28, 2024
Acute kidney injury (AKI) following multiple wasp stings is a severe complication with potentially poor outcomes. Despite extensive research on AKI's risk factors, predictive models for wasp sting-related AKI are limited. This study aims to develop a...
Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.
Scientific reports
Sep 27, 2024
Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. This study aims to integrate preoperative and intraoperative variables to compare traditional nomogra...
Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.
The international journal of cardiovascular imaging
Sep 25, 2024
Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fibrillation (AF), which means LAA thrombus/sludge, dense spontaneous echo contrast and slow LAA blood...
Anticoagulants
Blood Coagulation
Coronary Angiography
Radiographic Image Interpretation, Computer-Assisted
Atrial Fibrillation
Middle Aged
Risk Assessment
Nomograms
Predictive Value of Tests
Thrombophilia
Multidetector Computed Tomography
Computed Tomography Angiography
Atrial Appendage
Humans
Machine Learning
Decision Support Techniques
Retrospective Studies
Female
Reproducibility of Results
Risk Factors
Aged
Radiomics
Male
Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.
Frontiers in immunology
Sep 24, 2024
BACKGROUND: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene sign...
A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study.
JMIR cancer
Sep 20, 2024
BACKGROUND: Salvage radiation therapy (sRT) is often the sole curative option in patients with biochemical recurrence after radical prostatectomy. After sRT, we developed and validated a nomogram to predict freedom from biochemical failure.
Interpretable prediction of acute ischemic stroke after hip fracture in patients 65 years and older based on machine learning and SHAP.
Archives of gerontology and geriatrics
Sep 19, 2024
BACKGROUND: Hip fracture and acute ischemic stroke (AIS) are prevalent conditions among the older population. The prognosis for older patients who experience AIS subsequent to hip fracture is frequently unfavorable.
Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.
ESC heart failure
Sep 19, 2024
AIMS: This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care unit (ICU) with heart failure (HF).
Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators.
Frontiers in immunology
Sep 19, 2024
BACKGROUND: Moderately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improv...
Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study.
Academic radiology
Sep 16, 2024
RATIONALE AND OBJECTIVES: To develop and validate multimodal deep-learning models based on clinical variables, multiparametric MRI (mp-MRI) and hematoxylin and eosin (HE) stained pathology slides for predicting microsatellite instability (MSI) status...
Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.
Frontiers in immunology
Sep 16, 2024
OBJECTIVE: To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in chronic HBV infection (CBI) patients.
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