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Survival Analysis

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Integrating Machine Learning and Traditional Survival Analysis to Identify Key Predictors of Foveal Involvement in Geographic Atrophy.

Investigative ophthalmology & visual science
PURPOSE: The purpose of this study was to investigate the incidence of foveal involvement in geographic atrophy (GA) secondary to age-related macular degeneration (AMD), using machine learning to assess the importance of risk factors.

An online framework for survival analysis: reframing Cox proportional hazards model for large data sets and neural networks.

Biostatistics (Oxford, England)
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...

PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas.

Bioinformatics (Oxford, England)
MOTIVATION: Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or c...

Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data.

Briefings in bioinformatics
Biomedical multi-modality data (also named multi-omics data) refer to data that span different types and derive from multiple sources in clinical practices (e.g. gene sequences, proteomics and histopathological images), which can provide comprehensiv...

Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes.

Methods in molecular biology (Clifton, N.J.)
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...

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.

GigaScience
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...

Deep learning for survival analysis in breast cancer with whole slide image data.

Bioinformatics (Oxford, England)
MOTIVATION: Whole slide tissue images contain detailed data on the sub-cellular structure of cancer. Quantitative analyses of this data can lead to novel biomarkers for better cancer diagnosis and prognosis and can improve our understanding of cancer...

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.

International journal of epidemiology
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...

Survival prediction model for right-censored data based on improved composite quantile regression neural network.

Mathematical biosciences and engineering : MBE
With the development of the field of survival analysis, statistical inference of right-censored data is of great importance for the study of medical diagnosis. In this study, a right-censored data survival prediction model based on an improved compos...

The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning.

The journal of trauma and acute care surgery
BACKGROUND: Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable being the Injury Severity Score (ISS). Although easy to calculate, it requires additional administrati...