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Towards a Reporting Guideline for Studies on Information Extraction from Clinical Texts
Daniel Reichenpfader1, Kerstin Denecke1
1Bern University of Applied Sciences, Institute for Patient-centered Digital Health, Biel/Bienne, Switzerland.
Studies in Health Technology and Informatics
|August 23, 2024
Summary
This study introduces a reporting guideline to standardize information extraction (IE) from clinical texts, enhancing study comparability and replicability in clinical Natural Language Processing research.
Area of Science:
- Clinical Natural Language Processing
- Information Extraction
- Medical Informatics
Background:
- Rapid advancements in clinical Natural Language Processing (NLP) and information extraction (IE) have created challenges in study comparability and replicability.
- Lack of standardized reporting hinders the evaluation and integration of findings from clinical text analysis.
Purpose of the Study:
- To propose a novel reporting guideline for studies involving information extraction from clinical texts.
- To standardize the description of methodologies and outcomes in clinical IE research.
Main Methods:
- Development of the guideline based on experiences from a scoping review of 34 studies on IE from free-text radiology reports.
- Identification and definition of key reporting aspects across essential categories.
Main Results:
- The proposed guideline includes five top-level categories: information model, architecture, data, annotation, and outcomes.
- A total of 28 specific aspects are defined for reporting in clinical IE studies.
Conclusions:
- The guideline aims to establish a reporting standard for clinical text IE, promoting uniformity in the research field.
- Future work includes developing a consensus-based taxonomy and updating the guideline to accommodate technological advancements.
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