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Survival Analysis

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Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate.

Artificial intelligence in medicine
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationships. In particular, we model each subject as a distribution over "topics...

A deep learning approach for overall survival prediction in lung cancer with missing values.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical doma...

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Medical image analysis
Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model t...

Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model.

Clinical rheumatology
OBJECTIVE: The clinical manifestations of systemic sclerosis (SSc) are highly variable, resulting in varied outcomes and complications. Diverse fibrosis of the skin and internal organs, vasculopathy, and dysregulated immune system lead to poor and va...

deepAFT: A nonlinear accelerated failure time model with artificial neural network.

Statistics in medicine
The Cox regression model or accelerated failure time regression models are often used for describing the relationship between survival outcomes and potential explanatory variables. These models assume the studied covariates are connected to the survi...

Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.

Clinical and experimental medicine
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,8...

Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

European stroke journal
BACKGROUND: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and requi...

Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia.

Computers in biology and medicine
BACKGROUND: Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

Scientific reports
The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficien...

BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.

IEEE journal of biomedical and health informatics
Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, ...