AIMC Topic: Proportional Hazards Models
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An online framework for survival analysis: reframing Cox proportional hazards model for large data sets and neural networks.
Biostatistics (Oxford, England)
Dec 15, 2023
In many biomedical applications, outcome is measured as a "time-to-event" (e.g., disease progression or death). To assess the connection between features of a patient and this outcome, it is common to assume a proportional hazards model and fit a pro...
[Cox model analysis of curative effect and prognostic factors of oral robot-assisted RPLN dissection for head and neck malignancies].
Shanghai kou qiang yi xue = Shanghai journal of stomatology
Oct 1, 2023
PURPOSE: To investigate the efficacy and prognostic factors of oral robot-assisted retropharyngeal lymph node (RPLN) dissection in the treatment of head and neck malignancies.
Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2023
Discovering molecular biomarkers for predicting patient survival outcomes is an essential step toward improving prognosis and therapeutic decision-making in the treatment of severe diseases such as cancer. Due to the high-dimensionality nature of omi...
Image-Based Deep Neural Network for Individualizing Radiotherapy Dose Is Transportable Across Health Systems.
JCO clinical cancer informatics
Jan 1, 2023
PURPOSE: We developed a deep neural network that queries the lung computed tomography-derived feature space to identify radiation sensitivity parameters that can predict treatment failures and hence guide the individualization of radiotherapy dose. I...
VdistCox: Vertically distributed Cox proportional hazards model with hyperparameter optimization.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Vertically partitioned data is distributed data in which information about a patient is distributed across multiple sites. In this study, we propose a novel algorithm (referred to as VdistCox) for the Cox proportional hazards model (Cox model), which...
SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.
GigaScience
Jul 30, 2022
Survival analysis is a branch of statistics that deals with both the tracking of time and the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic stat...
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.
International journal of epidemiology
Jun 13, 2022
BACKGROUND: Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep le...
Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.
Age and ageing
Apr 1, 2022
BACKGROUND: ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).
Stratified neural networks in a time-to-event setting.
Briefings in bioinformatics
Jan 17, 2022
Deep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations i...
Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery.
Mayo Clinic proceedings
Dec 1, 2021
OBJECTIVE: To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality...
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