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Joint Models for Event Prediction from Time Series and Survival Data

We present a nonparametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions. Technical details are available in the supplementary materials.

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.63 No.4 November 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 477-486

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

REMAINING USEFUL LIFE
CONDITION MONITORING SIGNALS
GAUSSIAN CONVOLUTION PROCESS
SURVIVAL MODEL

Info Detail Spesifik

https://doi.org/10.1080/00401706.2020.1832582

Pernyataan Tanggungjawab

Xubo Yue, Raden Al Kontar

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