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

Author:

Seinen Tom M1ORCID,Fridgeirsson Egill A1,Ioannou Solomon1,Jeannetot Daniel1,John Luis H1,Kors Jan A1,Markus Aniek F1,Pera Victor1,Rekkas Alexandros1ORCID,Williams Ross D1ORCID,Yang Cynthia1ORCID,van Mulligen Erik M1,Rijnbeek Peter R1

Affiliation:

1. Department of Medical Informatics, Erasmus University Medical Center , Rotterdam, The Netherlands

Abstract

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.

Funder

European Health Data & Evidence Network

Innovative Medicines Initiative 2 Joint Undertaking

European Union’s Horizon 2020 research and innovation program and EFPIA

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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