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

TM Seinen*, JA Kors, EM van Mulligen, E Fridgeirsson, PR Rijnbeek

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
14 Downloads (Pure)

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, the combination of structured and text data outperformed models using structured or text data alone. No large performance differences were found between the different text representations and prediction algorithms.

Discussion:

Our findings indicate that the use of unstructured data alone can result in well-performing prediction models for some clinical prediction problems. Furthermore, the performance improvement achieved by combining structured and text data highlights the added value. Additionally, we demonstrate the significance of clinical natural language processing research in languages other than English and the possibility of validating text-based prediction models across various EHR systems.

Conclusion:

Our study highlights the potential benefits of incorporating unstructured data in clinical prediction models in a GP setting. Although the added value of unstructured data may vary depending on the specific prediction task, our findings suggest that it has the potential to enhance patient care.
Original languageEnglish
Pages (from-to)1973-1984
Number of pages12
JournalJournal of the American Medical Informatics Association
Volume30
Issue number12
Early online date16 Aug 2023
DOIs
Publication statusPublished - 1 Dec 2023

Bibliographical note

Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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