mailitics

Tag: survival

  • The Data Team’s Survival Guide for the Next Era of Data

    The Data Team’s Survival Guide for the Next Era of Data 6 pillars to declutter your stack, escape the service trap, and build the missing foundations for the new primary data consumer: the AI agent. The post The Data Team’s Survival Guide for the Next Era of Data appeared first on Towards Data Science. Mahdi…

  • WTNN: Weibull-Tailored Neural Networks for survival analysis

    WTNN: Weibull-Tailored Neural Networks for survival analysis arXiv:2512.09163v1 Announce Type: new Abstract: The Weibull distribution is a commonly adopted choice for modeling the survival of systems subject to maintenance over time. When only proxy indicators and censored observations are available, it becomes necessary to express the distribution’s parameters as functions of time-dependent covariates. Deep neural…

  • ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression

    ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression arXiv:2509.07108v1 Announce Type: new Abstract: Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictive performance. However, most of these models do not provide interpretable insights into the association between exposures and…

  • Counterfactual Survival Q Learning for Longitudinal Randomized Trials via Buckley James Boosting

    Counterfactual Survival Q Learning for Longitudinal Randomized Trials via Buckley James Boosting arXiv:2508.11060v1 Announce Type: new Abstract: We propose a Buckley James (BJ) Boost Q learning framework for estimating optimal dynamic treatment regimes under right censored survival data, tailored for longitudinal randomized clinical trial settings. The method integrates accelerated failure time models with iterative boosting…

  • Reduction Techniques for Survival Analysis

    Reduction Techniques for Survival Analysis arXiv:2508.05715v1 Announce Type: new Abstract: In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that “reduce” a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival…

  • Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction

    Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction arXiv:2507.14641v1 Announce Type: new Abstract: This research integrates deep learning, copula functions, and survival analysis to effectively handle highly correlated and right-censored multivariate survival data. It introduces copula-based activation functions (Clayton, Gumbel, and their combinations) to model the nonlinear dependencies inherent in such…

  • Survival Analysis When No One Dies: A Value-Based Approach

    Survival Analysis When No One Dies: A Value-Based Approach Survival Analysis is a statistical approach used to answer the question: “How long will something last?” That “something” could range from a patient’s lifespan to the durability of a machine component or the duration of a user’s subscription. One of the most widely used tools in…

  • TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis

    TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis arXiv:2505.01785v1 Announce Type: new Abstract: Estimating the causal effect of time-varying treatments on survival outcomes is a challenging task in many domains, particularly in medicine where treatment protocols adapt over time. While recent advances in representation learning have improved causal inference for static treatments, extending these methods…

  • Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests

    Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests arXiv:2504.18498v1 Announce Type: new Abstract: Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This…

  • On the Tunability of Random Survival Forests Model for Predictive Maintenance

    On the Tunability of Random Survival Forests Model for Predictive Maintenance arXiv:2504.14744v1 Announce Type: new Abstract: This paper investigates the tunability of the Random Survival Forest (RSF) model in predictive maintenance, where accurate time-to-failure estimation is crucial. Although RSF is widely used due to its flexibility and ability to handle censored data, its performance is…

  • Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data

    Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data arXiv:2503.09097v1 Announce Type: new Abstract: In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep learning methods for analyzing censored time-to-event data, especially when dealing with high-dimensional predictors that are complexly interrelated. Many…

  • Practical Evaluation of Copula-based Survival Metrics: Beyond the Independent Censoring Assumption

    Practical Evaluation of Copula-based Survival Metrics: Beyond the Independent Censoring Assumption arXiv:2502.19460v1 Announce Type: new Abstract: Conventional survival metrics, such as Harrell’s concordance index and the Brier Score, rely on the independent censoring assumption for valid inference in the presence of right-censored data. However, when instances are censored for reasons related to the event of…

  • The Intuition behind Concordance Index — Survival Analysis

    The Intuition behind Concordance Index — Survival Analysis The Intuition behind Concordance Index — Survival Analysis Ranking accuracy versus absolute accuracy Taken by the author and her Border Collie. “Be thankful for what you have. Be fearless for what you want” How long would you keep your Gym membership before you decide to cancel it? or Netflix if you are a series…