Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Zheng, Zhichang; Guo, Ruifeng; Wang, Nian; Jiang, Bo; Peng, Chun; Ai, Hui; Wang, Xiao; Nie, Shaoping

doi:10.1155/2022/1795588

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…In the context of financial institutions, ML algorithms can detect anomalies in transactional data that may indicate fraudulent activities, such as unusual spending patterns or unauthorized account access. By automatically flagging suspicious transactions, ML algorithms enable financial institutions to respond swiftly to potential threats and enhance fraud detection capabilities (Zheng, 2018). Furthermore, ML algorithms can improve portfolio risk management by identifying correlations and dependencies among different asset classes, enabling financial institutions to optimize portfolio diversification strategies and mitigate systemic risks (Yang, 2020).…”

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confidence: 99%

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Mwangi

2024

IJMRM

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Purpose: The aim of the study was to examine the role of machine learning in enhancing risk management strategies in financial institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that integration of machine learning into risk management strategies within financial institutions has demonstrated significant potential for enhancing decision-making processes and mitigating various risks. The study have consistently shown that machine learning algorithms outperform traditional statistical methods in areas such as credit risk assessment, fraud detection, market risk management, and loan portfolio optimization. These advancements have led to improved accuracy, efficiency, and timeliness in risk assessment, enabling financial institutions to make more informed decisions while reducing losses and enhancing overall performance. Unique Contribution to Theory, Practice and Policy: Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH) & Agency Theory may be used to anchor future studies on role of machine learning in enhancing risk management strategies in financial institutions. Invest in building robust data infrastructure and governance frameworks to support the implementation of machine learning models in risk management practices. High-quality data is crucial for training accurate and reliable machine learning algorithms. Establish regulatory guidelines and standards for the responsible use of machine learning in risk management within the financial industry. These guidelines should address issues such as model transparency, fairness, and accountability to ensure ethical and responsible practices.

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Section: Introductionmentioning

confidence: 99%

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Mwangi

2024

IJMRM

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Coronary computed tomography angiography-derived fat attenuation index predict future percutaneous coronary intervention

He,

Lu,

Yin

et al. 2024

British Journal of Radiology

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Objectives This study aims to investigate the differences in plaque characteristics and fat attenuation index (FAI) between in patients who received revascularization versus those who did not receive revascularization and examine whether the machine-learning (ML) based model constructed by plaque characteristics and FAI can predict revascularization. Materials & Methods This study was a post hoc analysis of a prospective single-center registry of sequential patients undergoing CCTA, referred from inpatient and emergency department settings (n = 261, 63 years ± 8; 188 men). The primary outcome was revascularization by percutaneous coronary revascularization. The CTA images were analyzed by experienced radiologists using a dedicated workstation in a blinded fashion. The ML-based model was automatically computed. Results The study cohort consisted of 261 subjects. Revascularization was performed in 105 subjects. Patients receiving revascularization had higher FAI value (67.35±5.49 Hu vs -80.10±7.75 Hu, p < 0.001) as well as higher plaque length, calcified, lipid and fibrous plaque burden and volume. When FAI was incorporated into a ML risk model based on plaque characteristics to predict revascularization, the area under the curve increased from 0.84 (95% CI: 0.68—0.99) to 0.95 (95% CI: 0.88—1.00). Conclusion ML-algorithms based on FAI and characteristics could help improve the prediction of future revascularization and identify patients likely to receive revascularization. Advances in knowledge Pre-procedural FAI could help guide revascularization in symptomatic CAD patients.

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Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Zheng

Guo

Wang

et al. 2022

Journal of Healthcare Engineering

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Objective. The study aimed to use machine learning algorithms to predict the need for revascularization in patients presenting with chest pain in the emergency department. Methods. We obtained data from 581 patients with chest pain, 264 who underwent revascularization, and the other 317 were treated with medication alone for 3 months. Using standard algorithms, linear discriminant analysis, and standard algorithms, we analyzed 41 features relevant to coronary artery disease (CAD). Results. We identified seven robust predictive features. The combination of these predictors gave an area under the curve (AUC) of 0.830 to predict the need for revascularization. By contrast, the GRACE score gave an AUC of 0.68. Conclusions. This machine learning-based approach predicts the need for revascularization in patients with chest pain.

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Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

Cited by 3 publications

References 19 publications

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Coronary computed tomography angiography-derived fat attenuation index predict future percutaneous coronary intervention

Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

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