ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
A deep-learning based system for accurate extraction of blood pressure data in clinical narratives.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a novel workflow for identifying and analyzing blood pressure readings in clinical narratives using a Convolution Neural Network. The network performs three tasks: identifying blood pressure readings, determining the exactness of the readings, and then classifying the readings into three classes: general, treatment, and suggestion. The system can be easily set up and deployed by people who are not experts in clinical Natural Language Processing. The validation results on an independent test set show the first two of the three tasks achieve a precision, recall, and F-measure over or close to 95%, and the third task achieves an overall accuracy of 85.4%. The study demonstrates that the proposed workflow is effective for extracting blood pressure data in clinical notes. The workflow is general and can be easily adapted to analyze other clinical concepts for phenotyping tasks.
Identifiers
32477693PMC7233038What 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.