Acute coronary syndrome prediction in emergency care: A machine learning approach

Emakhu, Joshua; Monplaisir, Leslie; Aguwa, Celestine; Arslanturk, Suzan; Masoud, Sara; Nassereddine, Hashem; Hamam, Mohamed Serhan; Miller, Joseph

doi:10.1016/j.cmpb.2022.107080

Cited by 10 publications

(9 citation statements)

References 24 publications

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“…AI can provide early alerts and monitor vital signs and other patient data to anticipate adverse events, facilitating the timely identification of severe health conditions ( 28 , 33 , 42 ). Concurrently, learning using AI models can augment the precision and efficiency of diagnoses and prognoses for emergency personnel ( 34 , 40 , 43 ). An example is the pulmonary embolism result forecast model (PERFORM), which uses high patient data volumes to predict health outcomes.…”

Section: Bibliographic Analysis Of Emergency Department Ai Opportunit...mentioning

confidence: 99%

Designing Chinese hospital emergency departments to leverage artificial intelligence—a systematic literature review on the challenges and opportunities

Tan,

Mills

2024

Front. Med. Technol.

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Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.

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Section: Bibliographic Analysis Of Emergency Department Ai Opportunit...mentioning

confidence: 99%

Designing Chinese hospital emergency departments to leverage artificial intelligence—a systematic literature review on the challenges and opportunities

Tan,

Mills

2024

Front. Med. Technol.

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“…In the healthcare sector, Machine Learning (ML) finds application across diverse areas, encompassing disease diagnosis [4][5][6][7], patient management [8][9][10][11], and administrative tasks [12][13][14][15]. In the realm of diagnosis, ML contributes to the analysis of medical images, including X-rays [16,17], MRIs [18][19][20], and CT scans [21,22], facilitating early disease detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Analysis of Endoscope Demand in Otolaryngology Outpatient Settings

Lanier,

Roush,

Young

et al. 2024

BioMedInformatics

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Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for predicting and managing clinical demand for flexible endoscopes. The HLD disinfection process is time-consuming and requires specialized training and competency to be performed safely. Solely depending on human expertise for predicting the flexible endoscope demands is unreliable and produced a concern of an inadequate supply of devices available for diagnostic purposes. Method: The demand for flexible endoscopes for future patient visits has not been well studied but can be modeled based on patients’ historical information, provider, and other visit-related factors. Such factors are available to the clinic before the visit. Binary classifiers can be used to help inform the sterile processing department of reprocessing needs days or weeks earlier for each patient. Results: Among all our trained models, Logistic Regression reports an average AUC ROC score of 89% and accuracy of 80%. Conclusion: The proposed framework not only significantly reduces the reprocessing efforts in terms of time spent on communication, cleaning, scheduling, and transferring scopes, but also helps to improve patient safety by reducing the exposure risk to potential infections.

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“…The concrete use of the ML models in clinical practice as well as the potential impact of errors was not addressed and not included in the evaluation, in these cases. In (37), the main goal of the development was the prediction of an acute coronary syndrome. Additionally, a cost-sensitive approach was included in the evaluation of the models, besides the utilization of standardized metrics.…”

Section: Topic a -Utilization Of Risk-based Performance Metrics In Re...mentioning

confidence: 99%

“…• Case BII: The two cases ( 30) and (34), which addressed mortality prediction as the application of the ML model and which did not include any further risk-based elements in the evaluation of the models, were excluded. The paper (37), which included risk factors in the evaluation , were counted as the only remaining positive case. This led to an overall result of 1 in 28 cases, i.e.…”

Section: Topic a -Utilization Of Risk-based Performance Metrics In Re...mentioning

confidence: 99%

Risk-based Evaluation of ML Classification Methods Used for Medical Devices

Haimerl,

Reich

2023

Preprint

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Background: In the future, more and more medical devices will be based on machine learning (ML) methods. For such medical devices, the rating of risks is a crucial aspect and should be considered when evaluating their performance. This means that an integration of risks and their associated costs into the corresponding metrics should be taken into account. This paper addresses three key issues towards a risk-based evaluation of ML-based classification models. Methods: First, it analyzes a selected set of scientific publications for determining how often risk-based metrics are currently utilized in the context of ML-based classification models. Second, it introduces an approach for evaluating such models where expected risks and associated costs are integrated into the corresponding performance metrics. Additionally, it analyzes the impact of different risk ratios on the resulting overall performance. For this purpose, an artificial model was used which allows to easily adapt key parameters. Third, the paper elaborates how such risk-based approaches relate to regulatory requirements in the field of medical devices. A set of use case scenarios were utilized to demonstrate necessities and practical implications, in this regard. Results: With respect to the first research question, it was shown that currently most scientific publications do not include risk-based approaches for measuring performance. For the second topic, it was demonstrated that risk-based considerations have a substantial impact on the outcome. The relative increase of the resulting overall risks can go up 198%, i.e. the risk value almost triples, when the ratio between different types of risks (risk of false negatives in comparison to false positives) goes down/up to 0.1 or 10.0. As discussed within the third research question, this situation typically represents a case where the risk increases one level in the corresponding risk matrix. Based on this, it was demonstrated that differences in parameter settings lead to a substantially different behavior when risk factors are not addressed properly. Conclusion: In summary, the paper demonstrates the necessity of a risk-based approach for the evaluation of ML-based medical devices, develops basic steps towards such an approach, and elaborates consequences which occur, when these steps are neglected.

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Acute coronary syndrome prediction in emergency care: A machine learning approach

Cited by 10 publications

References 24 publications

Designing Chinese hospital emergency departments to leverage artificial intelligence—a systematic literature review on the challenges and opportunities

Designing Chinese hospital emergency departments to leverage artificial intelligence—a systematic literature review on the challenges and opportunities

Predictive Analysis of Endoscope Demand in Otolaryngology Outpatient Settings

Risk-based Evaluation of ML Classification Methods Used for Medical Devices

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