机器学习算法用于预测腹膜透析患者的不良预后
Jie Yang1, Jingfang Wan1, Lei Feng1,2
1Department of Nephrology, Daping Hospital, Army Medical University, Chongqing, 400042, China.
BMC medical informatics and decision making
|January 3, 2024
概括
机器学习模型可以预测腹透析 (PD) 患者的不良结果. 这项研究开发了一种CatBoost模型,用于识别高风险患者,有助于透析前查和透析后管理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 腹腔透析 (PD) 是一种关键的置换疗法.
- 缺少PD患者不良预后的准确预测模型存在.
- 这项研究旨在开发一种机器学习 (ML) 模型,用于预测PD患者的不良结果.
研究的目的:
- 构建和验证基于机器学习 (ML) 的预测模型,用于在经过腹膜透析 (PD) 的患者中预测不良预后.
- 通过使用先进的ML技术,确定影响患者预后的关键指标.
- 创建一个临床上适用于患者分层和管理的模型.
主要方法:
- 从2007年8月至2020年12月接受PD的824名患者的回顾性分析.
- 使用五种常见的ML算法进行初始模型训练,通过AUC和ACC评估性能.
- 使用SHAP值进行特征重要性分析,为一个紧,临床相关的模型选择前20个指标.
主要成果:
- 分类增强分类器 (CatBoost) 模型在完整的 (AUC=0.80,ACC=0.78) 和压缩的 (AUC=0.79,ACC=0.74) 形式中都表现出卓越的性能.
- 共有353名患者 (42.8%) 经历了不良结果,包括转换为血液透析或死亡.
- 压缩的CatBoost模型利用了20个关键功能,保持了强大的预测准确度.
结论:
- 开发的CatBoost预测模型,由ML提供动力,在预测PD患者的不良预后方面具有显著的潜力.
- 该模型可以帮助在透析前对患者进行查,并为PD开始后的层次管理策略提供信息.
- 该研究强调了ML在增强PD管理临床决策方面的实用性.
更多相关视频
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
6.8K
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
相关概念视频
01:27Dialysis
320
Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
320
01:24Kaplan-Meier Approach
148
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
148
01:21Cancer Survival Analysis
355
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
355