Machine Learning-Based Overall Survival Prediction of Elderly Patients With Multiple Myeloma From Multicentre Real-Life Data.

ABSTRACT:

Objective

To use machine learning methods to explore overall survival (OS)-related prognostic factors in elderly multiple myeloma (MM) patients.

Methods

Data were cleaned and imputed using simple imputation methods. Two data resampling methods were implemented to facilitate model building and cross validation. Four algorithms including the cox proportional hazards model (CPH); DeepSurv; DeepHit; and the random survival forest (RSF) were applied to incorporate 30 parameters, such as baseline data, genetic abnormalities and treatment options, to construct a prognostic model for OS prediction in 338 elderly MM patients (>65 years old) from four hospitals in Beijing. The C-index and the integrated Brier score (IBwere used to evaluate model performances.

Results

The 30 var

SUBMITTER: Bao L 

PROVIDER: S-EPMC9293757 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature