Multicohort study testing the generalisability of the SASKit-ML stroke and PDAC prognostic model pipeline to other chronic diseases.

ABSTRACT:

Objectives

To validate and test the generalisability of the SASKit-ML pipeline, a prepublished feature selection and machine learning pipeline for the prediction of health deterioration after a stroke or pancreatic adenocarcinoma event, by using it to identify biomarkers of health deterioration in chronic disease.

Design

This is a validation study using a predefined protocol applied to multiple publicly available datasets, including longitudinal data from cohorts with type 2 diabetes (T2D), inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and various cancers. The datasets were chosen to mimic as closely as possible the SASKit cohort, a prospective, longitudinal cohort study.

Data sources

Public data were used from the T2D (77 patients with potential pre-dia

SUBMITTER: Palmer D 

PROVIDER: S-EPMC11448215 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature