The Algorithms And Their Applications In Treatment Option Recommendation

In recent years,with the continuous development of medicine and computer science,the recommendation methods of treatment options have received more and more attention.The application of artificial intelligence related algorithms for the recommendation of medical solutions can improve medical diagnosis and assist doctors in making better diagnoses to benefit humanity.This article discusses the topic of treatment options recommendation algorithms and their applications.It introduces how to recommend as accurately as possible and as fast as possible.First,this article introduces knowledge-based medical recommendation methods and casebased medical recommendation methods.These two methods are used as the basis for the research in this paper,and other researches in this paper are based on this.For the knowledgebased recommendation method,this article introduces the decision tree structure and regular knowledge sources.For the case-based medical recommendation method,this article introduces feature engineering,the k N N algorithm,and uses the optimization algorithm to analyze the attributes in the feature vectors.The weights have been optimized.In case-based circumstances,recommendations for treatment that rely on historically similar patients are provided by the experts,and for the same patient,the opinions of the experts are often different,which makes the label itself with a certain Uncertainty.This paper discusses in detail the existing case-based treatment options recommendation methods that ignore the problem of sample label uncertainty,and analyzes label uncertainty based on DempsterShafer evidence theory to improve the performance of recommendations.Experiments show that considering the uncertainty of the label does improve the prediction effect of medical plan recommendation.When the medical recommendation method introduced in this article is applied,the system may be serviced and deployed in the cloud,and there may be a large number of visits at the same time,which places strict requirements on the calculation speed of the recommendation algorithm.Aiming at this problem,this paper uses the boundary tree algorithm to optimize the case-based medical solution recommendation method,and fully considers the uncertainty of the label itself.Experiments show that the proposed method can optimize the recommendation speed on the premise of ensuring the accuracy of the recommendation.Aiming at the practical application of the recommendation algorithm,this article introduces the system architecture of the recommendation system,the technology used by the system platform,and explains the recommendation process.