Results 31 to 40 of about 28,926,907 (345)
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks
AAAI Conference on Artificial Intelligence, 2018 Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, medicine and many other areas. A fundamental problem is to understand the relationship between the covariates and the (distribution of) survival times ...
Changhee Lee +3 more
semanticscholar +1 more source
Lymph node evaluation and survival after resection of colorectal cancer
Medicine Science, 2017 Recent studies have demonstrated that lymph nodes ratio (LNR) might provide a significant prognostic role for colorectal cancer. We retrospectively analyzed the data of the patients with colorectal cancer and assessed a possible correlation between lymph
Lutfi Soylu +3 more
doaj +1 more source
Advanced survival models for risk-factor analysis in scrapie [PDF]
, 2007 Because of the confounding effects of long incubation duration and flock management, accurate epidemiological studies of scrapie outbreaks are difficult to carry out.
Andréoletti, Olivier +6 more
core +3 more sources
lifelines: survival analysis in Python
Journal of Open Source Software, 2019 One frustration of data scientists and statisticians is moving between programming languages to complete projects. The most common two are R and Python. For example, a survival analysis model may be fit using R’s survival-package (Terry M Therneau, 2015)
Cameron Davidson-Pilon
semanticscholar +1 more source
mlr3proba: an R package for machine learning in survival analysis
Bioinform., 2020 Summary As machine learning has become increasingly popular over the last few decades, so too has the number of machine-learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support
R. Sonabend +4 more
semanticscholar +1 more source
Genetic architecture of rainbow trout survival from egg to adult [PDF]
, 2010 Survival from birth to a reproductive adult is a challenge that only robust individuals resistant to a variety of mortality factors will overcome. To assess whether survival traits share genetic architecture throughout the life cycle, we estimated ...
Kause, A. +5 more
core +2 more sources
MatSurv: Survival analysis and visualization in MATLAB
Journal of Open Source Software, 2020 Survival analysis is a set of methods for evaluating time-to-event data that is widely applied across research disciplines. For example, it is commonly used in clinical trials to compare the effect of treatments.
Jordan H. Creed, T. Gerke, A. Berglund
semanticscholar +1 more source
Deep learning cardiac motion analysis for human survival prediction [PDF]
, 2018 Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient ...
Bello, Ghalib A. +10 more
core +2 more sources
Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits [PDF]
, 2014 A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications.
Labouriau, Rodrigo +2 more
core +2 more sources
Current knowledge of amphibian diversity in Sumatra, and its significance for conservation
OryxThe amphibians of the Indonesian island of Sumatra are poorly known, despite it being recognized as a biodiversity hotspot. For determining conservation priorities, up-to-date knowledge of the state of amphibian diversity in Sumatra is crucial ...
Umilaela Arifin
doaj +1 more source