ReviewSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2020
[Present research situation and prospect for delirium recognition based on electronic medical record].
Review in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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, 0 citations in OpenAlex.
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Delirium is a common complication in elderly inpatients which could result in cognitive impairment, and increase the risk of disability, fall and mortality. Moreover, it could cause heavy social burden. Even with multiple bedside screening scales to detect delirium, the rate of missed diagnosis is still high. Maybe it is associated with the acute fluctuation and nocturnal onset of delirium. With the development of the intelligence and automation of the electronic medical record (EMR), previous studies have explored the use of EMR to identify delirium patients, and this method provides help for delirium diagnosis and prevention. In this paper, we reviewed and summarized the current situation of research on delirium recognition by EMR, and put forward the development prospect in this method in order to provide basis and lay a foundation for intelligent diagnosis of delirium.
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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.