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Related Experiment Video

Updated: May 11, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

A K-nearest neighbors survival probability prediction method.

D J Lowsky¹, Y Ding, D K K Lee

¹RAND Corporation, Santa Monica, CA 90407, USA. dlowsky@rand.org

Statistics in Medicine

|May 9, 2013

Summary

This study presents a new nonparametric survival prediction method for right-censored data. It uses nearest neighbors to accurately predict patient graft survival, outperforming standard models.

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Last Updated: May 11, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Area of Science:

Biostatistics
Medical Informatics
Survival Analysis

Background:

Survival prediction is crucial in medicine, especially for censored data.
Existing methods like the Cox model have limitations, particularly when assumptions are violated.

Purpose of the Study:

To introduce a novel nonparametric survival prediction method for right-censored data.
To assess the method's performance against established models using real and simulated data.

Main Methods:

A weighted Kaplan-Meier estimator is constructed using K most similar training observations based on covariates.
Similarity is determined using a metric on the covariate space.
The method was applied to kidney transplantation data and a simulated dataset violating proportional hazards.

Main Results:

The proposed method generated patient-specific graft survival distributions.
Performance was compared with the Cox model and random survival forests.
The new method showed promise, especially in scenarios where standard assumptions are not met.

Conclusions:

The developed nonparametric method offers a flexible approach to survival prediction with right-censored data.
It provides accurate patient-specific survival curves, even when proportional hazards assumptions are violated.
This method has potential applications in various medical fields requiring survival analysis.