AIMC Topic: Proportional Hazards Models
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Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease.
Annals of the American Thoracic Society
May 1, 2025
Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increased mortality. Early identification of these high-risk patients and accurate prediction of the onset...
Investigating AI Approaches for Survival Prediction in Chronic Lymphocytic Leukemia.
Studies in health technology and informatics
Apr 8, 2025
Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting s...
Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.
Statistics in medicine
Mar 15, 2025
Targeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these mo...
Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.
Briefings in bioinformatics
Mar 4, 2025
High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules,...
Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm.
Journal of clinical hypertension (Greenwich, Conn.)
Mar 1, 2025
Carotid-femoral pulse wave velocity (cf-PWV) is an important but difficult to obtain measure of arterial stiffness and an independent predictor of cardiovascular events and all-cause mortality. The objective of this study was to develop a predictive ...
MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC.
Liver international : official journal of the International Association for the Study of the Liver
Mar 1, 2025
BACKGROUND & AIMS: Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally valida...
Autoencoder techniques for survival analysis on renal cell carcinoma.
PloS one
Jan 1, 2025
Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of survival (like genetic alterations or transcriptomic patterns of gene expression) is one of the mos...
All-Cause Mortality Risk in Elderly Patients with Femoral Neck and Intertrochanteric Fractures: A Predictive Model Based on Machine Learning.
Clinical interventions in aging
Jan 1, 2025
INTRODUCTION: The aim of this study was to identify the influencing factors for all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.
Survival analysis of clear cell renal cell carcinoma based on radiomics and deep learning features from CT images.
Medicine
Dec 20, 2024
PURPOSE: To create a nomogram for accurate prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on computed tomography images.
Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.
Medicine
Sep 27, 2024
Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predict...
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