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作 者:孔粼 刘永芳 KONG Lin;LIU Yongfang(School of Pediatrics,Chongqing Medical University,Chongqing 400014,China)
出 处:《中国继续医学教育》2024年第10期190-194,共5页China Continuing Medical Education
基 金:重庆市科卫联合医学科研项目(2021MSXM112);重庆医科大学儿科学院教育教学研究项目(EY201916)。
摘 要:近年来,临床营养治疗已经逐渐成为各类疾病不可缺少的一线治疗手段。然而长期以来,由于学科发展明显滞后于临床需求,培养临床营养人才较为困难。由于疾病谱、治疗手段等与成人有较大区别,儿科临床营养教育更是长期面临教学师资缺乏、教学难度大、教学与实践脱节、学生主动学习能力不足等诸多难以克服的问题。在大数据时代及人工智能的快速发展背景下,通过智能化教育的手段克服上诸多困难逐渐成为可能。在前期挖掘好大数据及搭建好智能化平台的基础上,儿科临床营养教师把真实的临床情景置入智能化平台,学生可以通过平台进行模拟营养干预并预测干预效果,之后亦可根据智能体对营养支持的效果反馈,不断优化、迭代营养方案,最终实现精准化儿科临床营养决策,达到提高儿科临床营养技能的最终目的。In recent years,clinical nutritional therapy has gradually become an indispensable first-line treatment method for various diseases.However,for a long time,it has been difficult to cultivate clinical nutrition talents due to the significant lag in disciplinary development compared to clinical needs.Due to significant differences in disease spectrum and treatment methods compared to adults,pediatric clinical nutrition education has long faced many insurmountable problems such as a lack of teaching staff,high teaching difficulty,disconnection between teaching and practice,and insufficient students'ability to actively learn.In the era of big data and the rapid development of artificial intelligence,it is gradually becoming possible to overcome many difficulties through intelligent education.On the basis of mining big data and building an intelligent platform in the early stage,pediatric clinical nutrition teachers put real clinical scenarios into the intelligent platform.Students can simulate nutritional interventions and predict intervention effects through the platform.Afterwards,they can continuously optimize and iterate nutritional plans based on the feedback of intelligent agents on nutritional support,ultimately achieving precise pediatric clinical nutrition decision-making,to achieve the ultimate goal of improving pediatric clinical nutrition skills.
关 键 词:儿科 临床营养 营养教学 互联网 大数据 智能化 预测
分 类 号:G642[文化科学—高等教育学]
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