Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer.

ABSTRACT:

Introduction

There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records.

Methods

This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry.

Results

The Kaiser Permanente Southern California coh

SUBMITTER: Chen W 

PROVIDER: S-EPMC9822857 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature