Use of unstructured text in prognostic clinical prediction models: a systematic review

Tom M. Seinen*, Egill A. Fridgeirsson, Solomon Ioannou, Daniel Jeannetot, Luis H. John, Jan A. Kors, Aniek F. Markus, Victor Pera, Alexandros Rekkas, Ross D. Williams, Cynthia Yang, Erik M. Van Mulligen, Peter R. Rijnbeek

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

OBJECTIVE: This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance.

MATERIALS AND METHODS: We searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models.

RESULTS: We identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited.

CONCLUSION: The use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.

Original languageEnglish
Pages (from-to)1292-1302
Number of pages11
JournalJournal of the American Medical Informatics Association
Volume29
Issue number7
Early online date27 Apr 2022
DOIs
Publication statusPublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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