Towards a Reporting Guideline for Studies on Information Extraction from Clinical Texts

Reichenpfader, Daniel; Denecke, Kerstin (2024). Towards a Reporting Guideline for Studies on Information Extraction from Clinical Texts In: Mantas, John; Hasman, Arie; Demiris, George; Saranto, Kaija; Marschollek, Michael; Arvanitis, Theodoros N.; Ognjanović, Ivana; Benis, Arriel; Gallos, Parisis; Zoulias, Emmanouil; Adrikopoulou, Elisavet (eds.) Digital Health and Informatics Innovations for Sustainable Health Care Systems. Studies in Health Technology and Informatics: Vol. 316 (pp. 1669-1673). Amsterdam: IOS Press 10.3233/SHTI240744

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Background: The rapid technical progress in the domain of clinical Natural Language Processing and information extraction (IE) has resulted in challenges concerning the comparability and replicability of studies. Aim: This paper proposes a reporting guideline to standardize the description of methodologies and outcomes for studies involving IE from clinical texts. Methods: The guideline is developed based on the experiences gained from data extraction for a previously conducted scoping review on IE from free-text radiology reports including 34 studies. Results: The guideline comprises the five top-level categories information model, architecture, data, annotation, and outcomes. In total, we define 28 aspects to be reported on in IE studies related to these categories. Conclusions: The proposed guideline is expected to set a standard for reporting in studies describing IE from clinical text and promote uniformity across the research field. Expected future technological advancements may make regular updates of the guideline necessary. In future research, we plan to develop a taxonomy that clearly defines corresponding value sets as well as integrating both this guideline and the taxonomy by following a consensus-based methodology.

Item Type:

Book Section (Book Chapter)

Division/Institute:

School of Engineering and Computer Science > Institute for Patient-centered Digital Health
School of Engineering and Computer Science > Institute for Patient-centered Digital Health > AI for Health
School of Engineering and Computer Science

Name:

Reichenpfader, Daniel0000-0002-8052-3359;
Denecke, Kerstin0000-0001-6691-396X;
Mantas, John;
Hasman, Arie;
Demiris, George;
Saranto, Kaija;
Marschollek, Michael;
Arvanitis, Theodoros N.;
Ognjanović, Ivana;
Benis, Arriel;
Gallos, Parisis;
Zoulias, Emmanouil and
Adrikopoulou, Elisavet

Subjects:

R Medicine > R Medicine (General)
T Technology > T Technology (General)

ISBN:

9781643685335

Series:

Studies in Health Technology and Informatics

Publisher:

IOS Press

Language:

English

Submitter:

Kerstin Denecke

Date Deposited:

11 Sep 2024 09:49

Last Modified:

18 Sep 2024 10:58

Publisher DOI:

10.3233/SHTI240744

ARBOR DOI:

10.24451/arbor.22404

URI:

https://arbor.bfh.ch/id/eprint/22404

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