Application of Intelligent Decision Model in Pediatric Clinical Nutrition Teaching
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.
万方数据
维普
