arXiv:2605.23082v1 Announce Type: cross 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-varying effects to be manually specified, which is increasingly impractical on rich clinical datasets. We introduce KAPLAN-HR, a B-spline Kolmogorov-Arnold Network (KAN) for nonparametric estimation of the conditional hazard as a joint function of covariates and time. A single-layer KAPLAN-HR model recovers a GAM, w
The proliferation of rich clinical datasets is pushing the limitations of classical statistical methods, necessitating more advanced AI/ML approaches for survival analysis.
Advanced and more accurate prognostic models for survival analysis, enabled by AI, can significantly improve clinical decision-making, drug development, and personalized medicine.
The introduction of KOLMOGOROV-ARNOLD PrognostIC Learnable Activation Networks (KAPLAN) offers a nonparametric, data-driven approach to estimate conditional hazards, moving beyond manually specified interactions in traditional models.
- · Biotech and Pharma Companies
- · Healthcare Providers
- · AI/ML Researchers
- · Patients
- · Traditional Statistical Software Providers
- · Manual Model Development Workflows
More precise risk stratification and outcome prediction in various clinical settings.
Accelerated development of targeted therapies and personalized treatment protocols based on more accurate prognostics.
Potential for AI-driven platforms to fully automate complex prognostic modeling, shifting expertise requirements in healthcare analytics.
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Read at arXiv cs.LG