The added value of text from Dutch general practitioner notes in predictive modeling.

ABSTRACT:

Objective

This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting.

Materials and methods

We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems.

Results

On average, over all the different text representations and prediction algorithms, models only using text data performed better or similar to models using structured data alone in 2 prediction tasks. Additionally, in these 2 tasks,

SUBMITTER: Seinen TM 

PROVIDER: S-EPMC10654855 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature