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KANs advance survival analysis with new deep learning model

By PulseAugur Editorial · [2 sources] ·

Researchers have developed KAPLAN-HR, a new deep learning model based on Kolmogorov-Arnold Networks (KANs) for survival analysis. This model can estimate conditional hazard rates as a joint function of covariates and time, overcoming limitations of traditional methods that require manual specification of complex effects. Evaluations on six clinical datasets show KAPLAN-HR performs comparably to or better than existing statistical and deep learning survival analysis techniques. AI

IMPACT Introduces a novel deep learning architecture for survival analysis, potentially improving predictions in clinical and other time-to-event domains.

RANK_REASON Publication of a new academic paper detailing a novel machine learning model.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

KANs advance survival analysis with new deep learning model

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Stelios Boulitsakis Logothetis, Angela Wood, Pietro Li \`o ·

    KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

    arXiv:2605.23082v1 Announce Type: new Abstract: Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as the Cox model and generalised additive models (GAMs) require interactions and time…

  2. arXiv stat.ML TIER_1 English(EN) · Pietro Li ò ·

    KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

    Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as the Cox model and generalised additive models (GAMs) require interactions and time-varying effects to be manually specified, which…

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