Suppr超能文献

基于卡方病例推理模型的某些危及生命疾病的风险因素分析和死亡预测。

Risk Factors Analysis and Death Prediction in Some Life-Threatening Ailments Using Chi-Square Case-Based Reasoning (χ CBR) Model.

机构信息

Department of Computer Science and Technology, College of Information Science and Engineering, Ocean University of China, Qingdao, Shandong, China.

出版信息

Interdiscip Sci. 2018 Dec;10(4):854-874. doi: 10.1007/s12539-018-0283-6. Epub 2018 Jan 30.

Abstract

A wealth of data are available within the health care system, however, effective analysis tools for exploring the hidden patterns in these datasets are lacking. To alleviate this limitation, this paper proposes a simple but promising hybrid predictive model by suitably combining the Chi-square distance measurement with case-based reasoning technique. The study presents the realization of an automated risk calculator and death prediction in some life-threatening ailments using Chi-square case-based reasoning (χ CBR) model. The proposed predictive engine is capable of reducing runtime and speeds up execution process through the use of critical χ distribution value. This work also showcases the development of a novel feature selection method referred to as frequent item based rule (FIBR) method. This FIBR method is used for selecting the best feature for the proposed χ CBR model at the preprocessing stage of the predictive procedures. The implementation of the proposed risk calculator is achieved through the use of an in-house developed PHP program experimented with XAMP/Apache HTTP server as hosting server. The process of data acquisition and case-based development is implemented using the MySQL application. Performance comparison between our system, the NBY, the ED-KNN, the ANN, the SVM, the Random Forest and the traditional CBR techniques shows that the quality of predictions produced by our system outperformed the baseline methods studied. The result of our experiment shows that the precision rate and predictive quality of our system in most cases are equal to or greater than 70%. Our result also shows that the proposed system executes faster than the baseline methods studied. Therefore, the proposed risk calculator is capable of providing useful, consistent, faster, accurate and efficient risk level prediction to both the patients and the physicians at any time, online and on a real-time basis.

摘要

医疗体系中存在大量数据,但缺乏有效的分析工具来挖掘这些数据集隐藏的模式。为了解决这个问题,本文提出了一种简单而有前途的混合预测模型,通过适当结合卡方距离测量和基于案例的推理技术。本研究提出了一种使用卡方案例推理(χ CBR)模型的自动风险计算器和危及生命疾病死亡率预测的实现方法。所提出的预测引擎能够通过使用关键 χ 分布值来减少运行时间并加快执行过程。本工作还展示了一种新的特征选择方法,称为基于频繁项的规则(FIBR)方法。该 FIBR 方法用于在预测过程的预处理阶段为所提出的 χ CBR 模型选择最佳特征。通过使用内部开发的 PHP 程序和 XAMP/Apache HTTP 服务器作为托管服务器来实现风险计算器的实现。数据采集和基于案例的开发过程使用 MySQL 应用程序实现。我们的系统、NBY、ED-KNN、ANN、SVM、随机森林和传统 CBR 技术之间的性能比较表明,我们的系统产生的预测质量优于所研究的基线方法。实验结果表明,我们的系统在大多数情况下的准确率和预测质量都等于或大于 70%。我们的结果还表明,所提出的系统比所研究的基线方法执行速度更快。因此,所提出的风险计算器能够在任何时候为患者和医生提供有用、一致、快速、准确和高效的风险水平预测,在线实时进行。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验