相关实验视频

Updated: Jan 16, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

鼻癌复发的预后模型使用禁忌搜索算法

Yara Raslan¹, Mushabab Asiri², Ahmed M Maklad³

¹Computer Science Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt; Saudi Proton Therapy Center, Riyadh, Saudi Arabia.

Computational biology and chemistry

|October 1, 2025

概括

这项研究引入了一种塔布搜索分类方法 (TSCM) 来预测鼻癌 (NPC) 复发. 由人工智能驱动的系统通过准确识别有风险的个体来增强治疗策略并改善患者的治疗结果.

科学领域:

瘤学和人工智能的人工智能
医疗保健中的计算智能
数据挖掘和预测分析.

背景情况:

复发性鼻癌 (NPC) 具有显著的死亡风险,需要改进治疗策略.
超听觉算法 (MH) 和数据挖掘在医疗保健中越来越重要,用于诊断和预测.
现有的NPC复发的预后需要加强,以改善患者的生存率和生活质量.

研究的目的:

开发一个人工咨询医疗保健系统 (AAHS) 来预测NPC复发.
通过准确的复发预测,提高治疗方案和患者的生存率.
利用人工智能来改善NPC的理解和管理.

主要方法:

采用了 Tabu Search (TS) 算法,并增强了动态社区结构 (DNHS),用于数据挖掘挑战.
使用患者数据开发了三种预测模型,在不同治疗阶段结合了越来越多的特征.
将这些模型集成到AAHS中,用于实时复发预测和治疗调整.

主要成果:

拟议的塔布搜索分类器方法 (TSCM) 准确预测NPC在不同治疗阶段的复发情况.
与现有的NPC复发预测相比,这三个预测模型显示出更高的性能.
根据预测的复发风险,AAHS可以及时调整治疗计划.

结论:

开发的AAHS有效预测NPC复发,帮助瘤学家主动管理患者.
这种人工智能驱动的方法在预测和防止NPC复发方面取得了重大进展.
该研究强调了元启发算法和数据挖掘在个性化癌症护理中的潜力.

关键词:

数据挖掘是一种数据挖掘.机器学习方法机器学习方法医疗数据分类医疗数据分类超启发式方法的方法.鼻癌的复发鼻癌的复发塔布搜索塔布搜索塔布搜索

更多相关视频

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma

Published on: April 20, 2018

相关概念视频

Cancer Survival Analysis

Cancer Survival Analysis

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...

Tumor Progression

Tumor Progression

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...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...