[论文解读] Enhancing Cardiovascular Disease Risk Prediction with Machine Learning Models

Farnoush Shishehbori, Zainab Awan|arXiv (Cornell University)|Jan 29, 2024

Artificial Intelligence in Healthcare被引用 7

一句话总结

本文综述了机器学习和深度学习模型如何在传统评分之外改善心血管疾病风险预测，讨论数据集、方法、挑战与临床影响。

ABSTRACT

Cardiovascular disease remains a leading global cause of mortality, necessitating accurate risk prediction tools. Traditional methods, such as QRISK and the Framingham heart score, exhibit limitations in their ability to incorporate comprehensive patient data, potentially resulting in incomplete risk factor consideration. To address these shortcomings, this study conducts a meticulous review focusing on the application of machine learning models to enhance predictive accuracy. Machine learning models, such as support vector machines, and Random Forest, as well as deep learning techniques like convolutional neural networks and recurrent neural networks, have emerged as promising alternatives. These models offer superior performance, accommodating a broader spectrum of variables and providing precise subgroup-specific predictions. While machine learning integration holds promise for enhancing risk assessment, it presents challenges such as data requirements and computational constraints. Additionally, large language models have revolutionised healthcare applications, augmenting diagnostic precision and patient care. This study examines the core aspects of cardiovascular disease event risk and presents a thorough review of traditional and machine learning models, alongside deep learning techniques, for improved accuracy. It offers a comprehensive survey of relevant datasets, critically compares ML models with conventional approaches, and synthesizes key findings, highlighting their implications for clinical practice. Furthermore, the potential of machine learning and large language models in cardiovascular medicine is undeniable. However, rigorous validation and optimisation are imperative before widespread application in healthcare. This integration promises more accurate and personalised cardiovascular care.

研究动机与目标

• 推动对超越传统工具如 QRISK 与 Framingham 的改进心血管疾病风险预测的需求。
• 评估机器学习模型如何纳入更广泛的患者数据以提升准确性。
• 综述一系列模型（支持向量机、随机森林、卷积神经网络、循环神经网络）及其在心血管疾病风险中的适用性。
• 强调数据需求、计算和临床转化方面的挑战，以及大型语言模型的潜在作用。

提出的方法

• 审查传统的 CVD 风险模型及其在整合多样化患者数据方面的局限性。
• 总结并比较用于心血管疾病风险预测的机器学习和深度学习方法。
• 讨论与基于机器学习的心血管疾病风险评估相关的数据源和数据集。
• 对模型性能、泛化能力及临床整合考虑因素进行批判性分析。
• 探索大型语言模型在增强心血管医学中的作用。
• 综合研究结果，为临床部署前的验证和优化提供指南。

实验结果

研究问题

• RQ1 传统心血管风险评分在捕捉全面患者数据方面的局限性是什么？
• RQ2 哪些机器学习和深度学习模型在改善心血管疾病风险预测方面具有潜力，以及在何种数据条件下？

主要发现

• ML and DL models can accommodate a broader range of variables than traditional scores, potentially improving prediction accuracy.
• There are methodological and data-related challenges, including data requirements and computational constraints.
• Large language models have the potential to augment diagnostic precision and patient care in cardiovascular medicine.
• Rigorous validation and optimization are essential before widespread clinical deployment.
• The study provides a comprehensive survey of datasets and compares ML approaches with conventional methods.
• There is potential for more personalized cardiovascular care with ML, contingent on robust validation.