从使用可解释机器学习的纵向多模式临床数据发现AML疾病进展的动态模型
Reza Mousavi1, Moaath K Mustafa Ali2, Daniel Lobo1,3
1Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.
medRxiv : the preprint server for health sciences
|April 29, 2025
概括
本研究引入了一种可解释的机器学习方法,使用患者数据预测急性髓性白血病 (AML) 的进展. 该方法准确预测疾病动态,为其他急性疾病提供潜力.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 急性髓性白血病 (AML) 是一种复杂的,具有高死亡率的侵袭性癌症.
- 有效的预测模型需要整合纵向患者数据.
- 了解疾病动态对于研究和临床应用至关重要.
研究的目的:
- 开发一个强大的方法来发现AML进展的动态预测模型.
- 阐明影响AML疾病动态的临床,遗传和治疗特征.
- 创建一个可解释的机器学习算法来预测AML的进展.
主要方法:
- 使用了AML患者的新型纵向多模式临床数据集.
- 采用高性能进化计算来解释机器学习算法.
- 发现了数学模型,包括相互作用,参数和节点,预测AML进展.
主要成果:
- 该方法准确估计了AML的临床动态,特别是爆炸百分比.
- 预测在培训和新型患者队列中得到了验证.
- 确定了调节疾病进展的关键临床,遗传和治疗特征.
结论:
- 可解释的机器学习方法成功地使用异构的纵向数据预测了AML的进展.
- 这种方法证明了模拟其他急性疾病进展动态的巨大潜力.
- 为推进瘤学临床和转化研究提供灵活的框架.
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
6.6K
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
1.0K
相关概念视频
Tumor Progression
6.1K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
6.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
25
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
25
Mechanistic Models: Compartment Models in Individual and Population Analysis
13
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
13
