ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2020
Deep Learning Identification of Asthma Inhaler Techniques in Clinical Notes.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
There are significant variabilities in clinicians' guideline-concordant documentation in asthma care. However, assessing clinicians' documentation is not feasible using only structured data but requires labor intensive chart review of electronic health records. Although the national asthma guidelines are available it is still challenging to use them as a real-time tool for providing feedback on adhering documentation guidelines for asthma care improvement. A certain guideline element, such as teaching or reviewing inhaler techniques, is difficult to capture by handcrafted rules since it requires contextual understanding of clinical narratives. This study examined a deep learning based natural language model, Bidirectional Encoder Representations from Transformers (BERT) coupled with distant supervision to identify inhaler techniques from clinical narratives. The BERT model with distant supervision outperformed the rule-based approach and achieved performance gain compared with the BERT without distant supervision.
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What 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.