AIMC Topic: Survival Analysis
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A support vector machine-based cure rate model for interval censored data.
Statistical methods in medical research
Nov 8, 2023
The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic funct...
AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images.
Medical image analysis
Nov 2, 2023
The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generall...
Deep Learning-Based Survival Analysis for Receiving a Steatotic Donor Liver Versus Waiting for a Standard Liver.
Transplantation proceedings
Oct 22, 2023
BACKGROUND: An emerging strategy to expand the donor pool is the use of a steatotic donor liver (SDLs; ≥ 30% macrosteatosis on biopsy). With the obesity epidemic and prevalence of nonalcoholic fatty liver disease, SDLs have been reported in 59% of al...
Metaparametric Neural Networks for Survival Analysis.
IEEE transactions on neural networks and learning systems
Aug 4, 2023
Survival analysis is a critical tool for the modeling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex machinery. However, current neural network models provide an imperfect solutio...
Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the SEER database and a multicentre external validation cohort.
Journal of cancer research and clinical oncology
Jul 10, 2023
PURPOSE: Due to the rarity of primary gastrointestinal lymphoma (PGIL), the prognostic factors and optimal management of PGIL have not been clearly defined. We aimed to establish prognostic models using a deep learning algorithm for survival predicti...
Survival analysis using deep learning with medical imaging.
The international journal of biostatistics
Jun 14, 2023
There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling...
Estimation of separable direct and indirect effects in a continuous-time illness-death model.
Lifetime data analysis
Jun 4, 2023
In this article we study the effect of a baseline exposure on a terminal time-to-event outcome either directly or mediated by the illness state of a continuous-time illness-death process with baseline covariates. We propose a definition of the corres...
18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival.
Contrast media & molecular imaging
May 4, 2023
Colon cancer is a type of cancer that begins in the large intestine. In the process of efficacy evaluation, postoperative recurrence prediction and metastasis monitoring of colon cancer, traditional medical image analysis methods are highly dependent...
Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction.
Computer methods and programs in biomedicine
Apr 11, 2023
BACKGROUND AND OBJECTIVE: Survival analysis is widely applied for assessing the expected duration of patient status towards event occurrences such as mortality in healthcare domain, which is generally considered as a time-to-event problem. Patients w...
Deep Survival Analysis With Clinical Variables for COVID-19.
IEEE journal of translational engineering in health and medicine
Mar 14, 2023
OBJECTIVE: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. ...
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