AIMC Topic: Survival Analysis

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Estimation of separable direct and indirect effects in a continuous-time illness-death model.

Lifetime data analysis
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
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
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
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. ...

Self-supervised learning-based Multi-Scale feature Fusion Network for survival analysis from whole slide images.

Computers in biology and medicine
Understanding prognosis and mortality is critical for evaluating the treatment plan of patients. Advances in digital pathology and deep learning techniques have made it practical to perform survival analysis in whole slide images (WSIs). Current meth...

Survival analysis of localized prostate cancer with deep learning.

Scientific reports
In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthca...

Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal.

Computational and mathematical methods in medicine
Survival analysis deals with the expected duration of time until one or more events of interest occur. Time to the event of interest may be unobserved, a phenomenon commonly known as right censoring, which renders the analysis of these data challengi...

Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach.

Genes
Analysis of data with a censored survival response and high-dimensional omics measurements is now common. Most of the existing analyses are based on specific (semi)parametric models, in particular the Cox model. Such analyses may be limited by not ha...

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.

Scientific reports
Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in b...

DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis.

eLife
Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-c...