ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Generalized Extraction and Classification of Span-Level Clinical Phrases.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Span-based annotation framework for LLM-based clinical named entity recognition: development and validation using Korean emergency department notes.JAMIA open · 2025Article
- Extracting Social Determinants of Health from Dental Clinical Notes.Applied clinical informatics · 2025Article
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Much of the critical information in a patient's electronic health record (EHR) is hidden in unstructured text. As such, there is an increasing role for automated text extraction and summarization to make this information available in a way that can be quickly and easily understood. While many clinical note text extraction techniques have been examined, most existing techniques are either narrowly targeted or focus primarily on concept-level extraction, potentially missing important contextual information. In contrast, in this work we examine the extraction of several clinical categories at the phrase level, attempting to provide the necessary context while still keeping the extracted elements concise. To do so, we employ a three-stage pipeline which extracts categorized phrases of interest using clinical concepts as anchor points. Results suggest the proposed method achieves performance comparable to that of individual human annotators.
Indexed as
Identifiers
30815058PMC6371324What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.