Research On Key Technologies Of Artificial Intelligence For Clinical Decision Support

Clinical decision support system(CDSS)is important to society.The State Council has clearly emphasized the importance of research and development of CDSS based on artificial intelligence by issuing many policy documents.Based on clinical knowledge and artificial intelligence technology,CDSS provides decision support for doctors in diagnosis,treatment,risk prediction,rational medication of diseases and other aspects.In recent years,the rapid development of big data,natural language processing,knowledge graph,machine learning and other artificial intelligence technologies has brought new impetus to CDSS.In this dissertation,we give a detailed analysis of the overall structure and study the four core technologies of the CDSS.Our main works are summarized as follows,we(1)Constructed a systematic approach to build the medical knowledge graph(KG)from real-world electronic medical records(EMRs)This systematic approach is composed of seven steps,and we created new methods for four steps,respectively,relation extraction,property calculation,graph cleaning,and related-entity ranking.For relation extraction step,we proposed a novel quadruplet structure to represent medical knowledge instead of the classical triplet in KG.And a novel related-entity ranking function considering probability,specificity and reliability(PSR)is proposed.The established systematic approach can efficiently construct a highquality medical KG from large-scale EMRs.The proposed ranking function PSR achieves the best performance under all relations.Moreover,the obtained medical KG finds many successful applications due to its statistics-based quadruplet,such as the CDSS of Southwest Hospital.(2)Proposed a series of algorithms named Pr Trans X to embed the medical probabilistic knowledge graphIn medical knowledge graphs,the relations between head and tail entities are inherently probabilistic,that is the main difference from typical knowledge graphs in which the relations are deterministic.To address the challenge,we enhanced the existing Trans X(X=E/H/R/D/Sparse)algorithms to Pr Trans X by 1)constructing a mapping function between the score value and the probability and 2)introducing probability-based loss of triplets into the original margin-based loss function.The proposed Pr Trans X algorithms are performed on the medical KG obtained by above approach.The disease clustering result validates the efficacy of the learned embedding vector as entity’s semantic representation.Then,the embeddings are evaluated using link prediction and another medical prediction task,the Pr Trans X performs better than the corresponding Trans X model.(3)Designed an intelligent and scalable computing clinical decision engine based on mind mappingBased on such engine,medical experts and computer experts can customize the rule decision tree together.And such engine has built-in multiple medical calculation operators,that means most of the work can be done by configuring calculation expressions.In addition,the designed decision tree can support user-defined operators,which makes it very flexible,and builds the foundation for the later hybrid model of rule decision trees and machine learning models.We developed a CDSS of liver cancer based on the proposed intelligent clinical decision engine.This system supports the 14 important decision scenarios of liver cancer patients.And the results show that the engine is suitable for developing CDSS system based on clinical guidelines,and has certain advanced technology in this field.(4)Built a hybrid decision model by embedding machine learning models into rule decision treeThe accuracy of data-based CDSS is high,but its clinical acceptance is not high because of its poor interpretation and sometimes may not conform to the clinical guidelines.On the other side,the accuracy of knowledge-based CDSS is poor,but it is accepted more easily because of its good interpretation.To solve these problems,we built a hybrid decision model,which can embed the machine learning model into the middle and/or leaf nodes of the rule decision tree.It makes the best of the advantages of data modeling in the individual nodes on the premise that the whole model conforms to the guidelines.We applied this hybrid model to the recommendation of adjuvant therapy for colon cancer.Compared with the simple rule-based or simple machine learning model,the working mechanism of the hybrid model is closer to the real process of rational(in line with the guidelines)and perceptual(clinical experience)decision-making of clinicians.The experimental results also verified that it is superior to other methods in accuracy and interpretability.