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Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.
1Aurélie Névéol, LIMSI CNRS UPR 3251, Rue John von Neumann, Campus Universitaire d'Orsay, 91405 Orsay cedex, France,
Yearbook of Medical Informatics
|August 22, 2015
Summary
Clinical Natural Language Processing (NLP) research is advancing, with 2014 papers showing deeper text analysis methods for clinical data. These Natural Language Processing advancements are paving the way for practical healthcare applications with significant clinical impact.
Area of Science:
- * Clinical Natural Language Processing (NLP)
- * Biomedical Informatics
- * Health Services Research
Background:
- * The field of clinical Natural Language Processing (NLP) has seen significant advancements.
- * Access to clinical data has improved due to de-identification progress.
- * NLP techniques are increasingly applied to healthcare challenges.
Purpose of the Study:
- * To summarize recent research in clinical Natural Language Processing (NLP).
- * To present a selection of the best papers published in 2014 in clinical NLP.
- * To highlight the progress and impact of NLP in healthcare.
Main Methods:
- * Systematic literature review by IMIA Yearbook NLP section editors.
- * Focused search on NLP applied to clinical texts or outcomes.
- * Peer review of shortlisted papers by independent external reviewers.
Main Results:
- * Five foundational clinical NLP papers were identified.
- * Research tackled diverse clinical data sources: online forums, EHRs.
- * Advanced NLP methods addressed complex issues like negation and temporal analysis.
- * NLP applications included cancer patient triage and health information communication.
Conclusions:
- * Clinical NLP is a thriving interdisciplinary field.
- * NLP researchers and healthcare professionals are collaborating.
- * Clinical NLP is maturing for practical applications with significant clinical impact.
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