Development of a Clinical Decision Support System for Smart Algorithms in Emergency Medicine

Vogel, Stefan de; Reiswich, Andreas; Ritter, Zully; Schmucker, Michael; Fuchs, Ângela; Pischek-Koch, Kerstin; Wache, Stefanie; Esslinger, Katrin; Dietrich, Michael; Kesztyüs, Tibor; Krefting, Dagmar; Haag, Martin; Blaschke, Sabine

doi:10.3233/shti210900

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Machine learning (ML) is being widely used in CDSS owing to its usefulness in diagnosis, prognosis, pattern recognition, and imaging classification with profound processing speed and the comprehensive nature of analytic methods 3 . The emergency care domain is particularly suitable for the challenge of adopting ML-based CDSS, because of the need for rapid clinical decision-making by physicians 4 . Accordingly, attempts at developing ML-based CDSS that enable efficient prediction for clinical practice have been reported in the setting of emergency departments (EDs) 3 , 5 – 7 .…”

Section: Introductionmentioning

confidence: 99%

“…Workflows are delayed in frequently crowded EDs 10 – 13 , making patients requiring time-critical interventions vulnerable to worse outcomes 14 – 16 . Therefore, it is crucial for CDSS to be able to assist the ED physicians who make time-critical decisions and interventions 4 . However, previous studies have been limited to a narrow range of input data and were inappropriate for ED use since they dealt with broad and prolonged outcomes 5 , 7 .…”

Section: Introductionmentioning

confidence: 99%

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Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department

Choi

Chung

et al. 2023

Sci Rep

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This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148,958 1 h-units. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department

Choi

Chung

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Unfortunately, the support of the hospital information system (HIS) for clinical decision-making is still insufficient [ 1 ]. Although many ED decision support systems (DSS) have been proposed [ 2 , 3 , 4 , 5 ], they are mostly research-oriented and have not been adopted for clinical use.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients

et al. 2022

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The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC’s AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard’s ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments.

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Clinical Decision Support System Braced with Artificial Intelligence: A Review

Prajapati

2022

Third International Conference on Image Processing and Capsule Networks

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Development of a Clinical Decision Support System for Smart Algorithms in Emergency Medicine

Cited by 8 publications

References 5 publications

Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department

Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department

Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients

Clinical Decision Support System Braced with Artificial Intelligence: A Review

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