机器学习对风险预测的增量价值在法洛特四分法中
Ayako Ishikita1, Chris McIntosh1,2,3, S Lucy Roche1
1Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, ON, Canada.
Heart (British Cardiac Society)
|December 1, 2023
概括
机器学习 (ML) 模型可以与专家临床医生相似地预测Fallot (rTOF) 的修复四位数中主要的不良心血管事件 (MACE). ML提高了对经验较少的临床医生的风险预测,接近经验丰富的专家的准确性.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 修复的Fallot四分法 (rTOF) 患者面临重大心血管不良事件 (MACE) 的风险.
- 准确预测MACE对于管理rTOF患者至关重要.
- 机器学习 (ML) 在心血管风险分层方面显示出潜力.
研究的目的:
- 评估ML的增量值与专家临床判断相比,用于预测rTOF的成年人5年的MACE.
- 评估ML是否可以提高风险预测的准确性,特别是对于经验较少的临床医生.
主要方法:
- 具有丰富经验的成人先天性心脏病 (ACHD) 临床医生确定了MACE预测的关键变量.
- 临床医生对rTOF患者的MACE风险 (低,中等,高) 进行了评估.
- 开发了一种经过验证的ML模型,并应用于同一个患者队列,以预测风险.
主要成果:
- ML预测性能 (AUC 0.85) 与综合专家评分 (AUC 0.92) 相似.
- 具有≥20年经验的专家表现出优异的预测 (AUC 0.98) 与<20年 (AUC 0.80) 的专家相比.
- 在经验较少的临床医生 (<20年) 中,ML整合改善了预测 (AUC 0.85),接近高度经验丰富的专家的表现.
结论:
- 机器学习和多学科专家团队都能在rTOF中稳定地预测5年的MACE.
- 对于某些临床医生来说,ML在风险预测方面提供了增量价值,在特定情况下提高了准确性.
相关概念视频
01:49Probability Laws
Overview
01:09Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:03Prediction Intervals
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...