Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department

Yu, Jae Yong; Jeong, Gab Yong; Jeong, Ok Soon; Chang, Dong Kyung; Chul, Won

doi:10.4258/hir.2020.26.1.13

Cited by 31 publications

(27 citation statements)

References 13 publications

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“…Through an emergency department triage system based on machine learning and the initial nursing assessment, an efficient system to predict adverse clinical outcomes could be created. This new system was more efficient than the previous traditional system [12].…”

Section: Objectivesmentioning

confidence: 82%

Artificial intelligence, machine learning, and deep learning in women’s health nursing

Jeong

2020

Korean J Women Health Nurs

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Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

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Section: Objectivesmentioning

confidence: 82%

Artificial intelligence, machine learning, and deep learning in women’s health nursing

Jeong

2020

Korean J Women Health Nurs

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“…A recent multinational study found that the National Early Warning Score (NEWS) can predict mortality among adult medical patients in the ED with an AUROC of 0.73, and the combination of the NEWS and laboratory biomarkers yielded an AUROC of 0.82 [ 16 ]. Some investigators developed machine-learning models for the prediction of death or admission to intensive care units among ED patients and reported the AUROCs of their models to be between 0.84 and 0.87 [ 15 , 28 ]. The AUROC of the scoring system for survival prediction among general ED patients developed in this study was 0.883, which can be regarded as adequate, considering the fact that patients with diverse complaints were included, no laboratory data are required, and the calculation is much simpler than the calculations needed for trauma scores based on anatomical injuries or sophisticated machine-learning-based models.…”

Section: Discussionmentioning

confidence: 99%

“…The relatively high rate of exclusion because of missing values may have caused selection bias; more cases were likely included from centers with relatively high proportions of patients with severe cases, which were also likely to have policies in place regarding the recording of vital signs and detailed medical records. Although some existing scoring systems have been derived from populations with fewer missing values, they tend to have been derived from the data obtained in a single hospital, including the Rapid Emergency Medicine Score (REMS) and ViEWS, which later served as a template for NEWS [ 15 , 29 – 31 ]. Our results are more reliable than the results in those studies because our data were derived from EDs at various levels and of different sizes; however, the amount of missing data was a limitation.…”

Section: Discussionmentioning

confidence: 99%

“…In the field of trauma care, methods of comparing performance between institutions or trauma systems have been developed and widely used, including the W statistic and standardized W (Ws) based on the Trauma and Injury Severity Score (TRISS) [ 6 , 7 , 10 – 14 ]. Although several scoring systems and machine learning-based approaches have been suggested to grade the severity of illness in ED patients, methods of comparing the severity-adjusted mortality of general ED patients between different systems have yet to be developed [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments

et al. 2021

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Background In-hospital mortality and short-term mortality are indicators that are commonly used to evaluate the outcome of emergency department (ED) treatment. Although several scoring systems and machine learning-based approaches have been suggested to grade the severity of the condition of ED patients, methods for comparing severity-adjusted mortality in general ED patients between different systems have yet to be developed. The aim of the present study was to develop a scoring system to predict mortality in ED patients using data collected at the initial evaluation and to validate the usefulness of the scoring system for comparing severity-adjusted mortality between institutions with different severity distributions. Methods The study was based on the registry of the National Emergency Department Information System, which is maintained by the National Emergency Medical Center of the Republic of Korea. Data from 2016 were used to construct the prediction model, and data from 2017 were used for validation. Logistic regression was used to build the mortality prediction model. Receiver operating characteristic curves were used to evaluate the performance of the prediction model. We calculated the standardized W statistic and its 95% confidence intervals using the newly developed mortality prediction model. Results The area under the receiver operating characteristic curve of the developed scoring system for the prediction of mortality was 0.883 (95% confidence interval [CI]: 0.882–0.884). The Ws score calculated from the 2016 dataset was 0.000 (95% CI: − 0.021 – 0.021). The Ws score calculated from the 2017 dataset was 0.049 (95% CI: 0.030–0.069). Conclusions The scoring system developed in the present study utilizing the parameters gathered in initial ED evaluations has acceptable performance for the prediction of in-hospital mortality. Standardized W statistics based on this scoring system can be used to compare the performance of an ED with the reference data or with the performance of other institutions.

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“…For the internal test data set, consisting of 1691 images (1075 malignant and 616 benign), and federated learning-trained deep learning algorithms, the accuracies of VGG19, SE-ResNet50, ResNet50, SE-ResNext50, and ResNext50 were 79.5%, 77.9%, 77.4%, 77.2%, and 73.9%, respectively (Table 2; Table S1 in Multimedia Appendix 1). Figure 3 shows the receiver operating characteristic curve [15] of each network for the internal test data set. Area under the receiver operating characteristic (AUROC) curve values of SE-ResNext50, ResNext50, VGG19, SE-ResNet50, and ResNet50 were 87.6%, 86.0%, 82.0%, 79.9%, and 78.9%, respectively.…”

Section: Federated Learning Performancementioning

confidence: 99%

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Lee¹,

Chai²,

Joo³

et al. 2021

JMIR Med Inform

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Background Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. Objective The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. Methods A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution. Results For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%. Conclusions We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.

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Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department

Cited by 31 publications

References 13 publications

Artificial intelligence, machine learning, and deep learning in women’s health nursing

Artificial intelligence, machine learning, and deep learning in women’s health nursing

Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

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