Oxygen Behavior in High Temperature Plasma and its Applications to Hydrocarbon Fuels

Uhm, Han S.; Choi, Chang Hun; Kim, Dong J.; Lee, Joo Y.; Woo, Sang Gyun

doi:10.3938/jkps.75.917

Cited by 1 publication

(1 citation statement)

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“…For oral mucosa PUs, nine photographs were used. The Krippendorff's α coefficient as a measure of inter-rater reliability was 0.75 (95% confidence interval, 0.46–0.85) 39 . The participants were asked to choose one of six options: step 0 to 3, not any stage, or “I don't know.” For both, the answers were classified as correct (coded 1) or not correct (coded 0).…”

Section: Methodsmentioning

confidence: 99%

Development and Effectiveness of a Clinical Decision Support System for Pressure Ulcer Prevention Care Using Machine Learning

Kim

Ryu²,

Choi³

2022

CIN: Computers, Informatics, Nursing

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This study was conducted to develop and evaluate the effectiveness of a clinical decision support system for pressure ulcer prevention on clinical (performance, visual discrimination ability, and decision-making ability) and cognitive (knowledge and attitude) workflow. After developing a clinical decision support system using machine learning, a quasiexperimental study was used. Data were collected between January and April 2020. Forty-nine RNs who met the inclusion criteria and worked at seven tertiary and five secondary hospitals participated. A clinical decision support system was provided to the intervention group during the same period. Differences in outcome variables between the two groups were analyzed using t tests. The level of pressure ulcer prevention nursing performance and visual differentiation ability of skin pressure and oral mucosa pressure ulcer showed significantly greater improvement in the experimental group compared with the control group, whereas clinical decision making did not differ significantly. A clinical decision support system using machine learning was partially successful in performance of skin pressure ulcer prevention, attitude, and visual differentiation ability for skin and oral mucosa pressure ulcer prevention. These findings indicated that a clinical decision support system using machine learning needs to be implemented for pressure ulcer prevention.

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Section: Methodsmentioning

confidence: 99%