个性化医疗中数据处理和使用的AI方法
AI Approaches in Processing and Using Data in Personalized Medicine
摘要：
在现代社会不断发展的社会中，越来越多的人患有慢性病和严重疾病，医生和患者需要特殊和复杂的医疗卫生支持。因此，重要的卫生利益攸关方已经认识到发展此类服务以使患者生活更轻松的重要性。这种支持需要收集大量的患者复杂数据，如临床，环境，营养，日常活动，来自智能可穿戴设备的各种数据，来自配备传感器的服装的数据等。必须正确汇总，处理，分析整体患者数据，并将其呈现给医生和护理人员，以推荐适当的治疗和行动，以改善患者的健康相关参数和整体健康。先进的人工智能技术提供了分析这些大数据、使用它们并获得新知识以支持个性化医疗决策的机会。基于先进机器学习、联邦学习、迁移学习、可解释人工智能等新方法为未来更高质量地使用健康和医疗数据开辟了新途径。在本文中，我们将提出一些关键的方面和特点的例子，在该领域的应用范围内的人工智能方法在个性化的医疗决策。
英文摘要：
In modern dynamic constantly developing society, more and more people suffer from chronic and serious diseases and doctors and patients need special and sophisticated medical and health support. Accordingly, prominent health stakeholders have recognized the importance of development of such services to make patients life easier. Such support requires the collection of huge amount of patients complex data like clinical, environmental, nutritional, daily activities, variety of data from smart wearable devices, data from clothing equipped with sensors etc. Holistic patients data must be properly aggregated, processed, analyzed, and presented to the doctors and caregivers to recommend adequate treatment and actions to improve patients health related parameters and general wellbeing. Advanced artificial intelligence techniques offer the opportunity to analyze such big data, consume them, and derive new knowledge to support personalized medical decisions. New approaches like those based on advanced machine learning, federated learning, transfer learning, explainable artificial intelligence open new paths for more quality use of health and medical data in future. In this paper, we will present some crucial aspects and characteristic examples in the area of application of a range of artificial intelligence approaches in personalized medical decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
