利用机器学习评估医疗效果的早期预警系统

Mohammed Abebe, Özlem Aktaş, Süleyman Sevinç
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引用次数: 0

摘要

基于人工智能的医疗变化跟踪和影响分析工具的开发可以对患者的实时康复产生有益的影响。该研究提出了一个使用机器学习的患者医疗变化跟踪和影响分析系统,特别是主成分分析和贝叶斯结构网络。我们发现所提出的系统在所有测试的患者数据中达到了可接受的统计显著性水平。此外,如果由于额外的缺失值和(或)新进行的医学测试导致变化而出现虚假变化,则因果影响分析能够将其视为虚假变化。因此,我们可以说,所提出的系统可以潜在地为临床医生提供实时监测和跟踪患者。此外,我们相信该方法在解释大量患者数据以建立危重患者的因果关系方面提供了一个有希望的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Early Warning System for Evaluating Effects of Medical Treatment using Machine Learning
The development of AI-based medical change tracking and impact analysis tools can have a beneficial effect on a patient's recovery in real-time. The study presents a system for patient medical change tracking and impact analysis using machine learning, particularly, principal component analysis and Bayesian structural networks. We found that the proposed system achieved an acceptable statistical significance level for all the patient data tested. Moreover, in cases where there are spurious changes due to extra missing values and/or newly administered medical tests causing the change, the causal impact analysis was able to capture them as bogus. Consequently, we can say that the proposed system can potentially offer real-time monitoring and tracking of patients for the clinicians. In addition, we believe that the approach provides a promising future in interpreting large quantities of patient data for establishing cause-effect relationships for critically ill patients.
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