A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

Wu, Ting; Zheng, Ruo Fei; Lin, Zhi Zhong; Gong, Hai Rong; Li, Hong

doi:10.1186/s12873-021-00501-8

“…Brajer et al suggested a machine-learning model fact sheet reporting for end-users 18 . Visualization-based efforts such as population, patient, and temporal level feature importance, or nomograms, could be adopted [19][20][21][22]…”

Section: Discussionmentioning

confidence: 99%

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department

Yu

¹

,

Xie

²

,

Liu

³

et al. 2022

Sci Rep

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Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients’ ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department.

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“…Brajer et al suggested a machine-learning model fact sheet reporting for end-users 18 . Visualization-based efforts such as population, patient, and temporal level feature importance, or nomograms, could be adopted [19][20][21][22] . Like the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) or The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting machine learning results 23,24 , there should be a guideline for the standardization of user interfaces (UIs) and a format for clinical decision support for end-users, including clinicians and patients.…”

Section: Discussionmentioning

confidence: 99%

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department

Yu

¹

,

Xie

²

,

Liu

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients’ ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department

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“…In the field of healthcare, artificial intelligence (AI) has a wide range of applications, including imaging and diagnostics, lifestyle management and supervision, nursing, emergency and hospital management, drug mining, virtual assistants, wearables, and more ( 13 , 14 ). AI in the healthcare industry can tremendously improve the efficiency of clinical work and reduce the shortage of medical resources.…”

Section: Introductionmentioning

confidence: 99%

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Zhou

¹

,

Wang

²

,

Feng

³

et al. 2022

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Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.

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A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

Cited by 15 publications

References 32 publications

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

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