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Article

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Title

Machine learning and natural language processing in clinical trial eligibility criteria parsing: a scoping review

Authors

[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

Drug Discovery Today

Journal year: 2024 | Journal volume: vol. 29 | Journal number: iss. 10

Article type

scientific article

Publication language

english

Keywords
EN
  • eligibility criteria
  • clinical trials
  • natural language processing
  • machine learning
  • cohort selection
Abstract

EN Automatic eligibility criteria parsing in clinical trials is crucial for cohort recruitment leading to data validity and trial completion. Recent years have witnessed an explosion of powerful machine learning (ML) and natural language processing (NLP) models that can streamline the patient accrual process. In this PRISMA-based scoping review, we comprehensively evaluate existing literature on the application of ML/NLP models for parsing clinical trial eligibility criteria. The review covers 9160 papers published between 2000 and 2024, with 88 publications subjected to data charting along 17 dimensions. Our review indicates insufficient use of state-of-the-art artificial intelligence (AI) models in the analysis of clinical protocols.

Pages (from - to)

104139-1 - 104139-8

DOI

10.1016/j.drudis.2024.104139

URL

https://www.sciencedirect.com/science/article/pii/S1359644624002642

Comments

Article Number: 104139

Ministry points / journal

200

Impact Factor

6,5 [List 2023]

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