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Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

机译:白血病中使用分子数据分析的智能技术:个性化药物支持系统的机会

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摘要

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.
机译:在医学中使用智能技术给白血病患者带来了希望。个性化治疗使用患者的遗传特征来选择治疗方式。该过程利用分子技术和机器学习来确定治疗白血病患者的最合适方法。迄今为止,从计算角度来看,还没有发表有关使用分子数据分析为白血病患者开发个性化药物智能技术的评论。这篇综述研究了已发表的关于白血病个性化药物的实证研究,并综合了与白血病智能技术有关的研究结果,特别关注这些研究的特定类别,以帮助寻找进一步研究慢性髓样白血病个性化药物支持系统的机会。进行了系统的搜索,以识别使用智能技术的白血病研究,并根据白血病类型以及研究的任务,数据来源和目的对这些研究进行分类。大多数研究将分子数据分析用于个性化医学,但是白血病患者的未来发展要求分子模型使用先进的机器学习方法来自动化治疗管理决策,以便在临床实践中向患者提供支持性医学信息。

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