Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data.

ABSTRACT:

Objectives

To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data.

Design

A single-centre, retrospective cohort study in China.

Setting

A Chinese central tertiary hospital.

Participants

The cohort study consisted of 1694 LN patients who had been registered in the Nanjing Glomerulonephritis Registry at the National Clinical Research Center of Kidney Diseases, Jinling Hospital from January 1985 to December 2010.

Methods

We developed a deep learning algorithm to predict LN relapse that consists of 59 features, including demographic, clinical, immunological, pathological and therapeutic characteristics that were collected for baseline analysis. A total of 32 227 data points w

SUBMITTER: Huang S 

PROVIDER: S-EPMC10941130 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature