Study On TCM Clinical Decision Support Based On Electronic Medical Record

In recent years,artificial intelligence has become a new driving force to promote social development,and is reshaping the operations across industries.One of the main applications of artificial intelligence in healthcare operations is to develop decision sup-port tools,which help to optimize clinical diagnosis and treatment decisions,provide more accurate diagnosis and treatment plans for patients,improve the quality and effi-ciency of healthcare services,and reduce medical errors and costs.With the wide appli-cation of information systems,medical institutions have accumulated massive medical data represented by electronic medical records(EMR).By mining the hidden knowl-edge and information in electronic medical records,it can provide decision support for clinical diagnosis and treatment.As the oldest traditional medicine in China,traditional Chinese medicine(TCM)has been used in the prevention and treatment of various dis-eases,and is an important part of healthcare in China.At present,the diagnosis and treatment decision of TCM mostly relies on the professional ability and clinical expe-rience of doctors,which is highly subjective,uneven in diagnosis and treatment levels,and lacks information-based clinical decision-making tools.Electronic medical records not only have structured data,but also include unstructured texts that hide a large number of useful information.However,the unstructured text usually contains a large amount of medical terms expressed in ancient Chinese with special syntactic structure and complex semantics,and it is a major challenge to extract useful information from it to optimize diagnosis and treatment decision-making.Therefore,the study of clinical decision sup-port based on TCM electronic medical records has important practical significance and theoretical value.Based on this background,this thesis aims to optimize diagnosis and treatment decision-making by data-driven method.Based on structured and unstructured data in electronic medical records,we use traditional machine learning,deep learning and nat-ural language processing methods to study the three key issues in clinical diagnosis and treatment of traditional Chinese medicine,such as the prediction of TCM syndromes,the automatic generation of prescription and the association learning of Chinese herbal medicine and the treatment efficacy.Finally,a clinical decision support system of tra-ditional Chinese medicine from diagnosis,prescription to efficacy evaluation is con-structed.The main contents and conclusions of this thesis are as follows:First,for the task of clinical diagnosis decision-making,Chapter 3 of this thesis explores the prediction of TCM syndromes based on the pre-training language model,which solves the problem of how to assist TCM diagnosis of patients.Our study mod-els the prediction of TCM syndromes as a text classification and directly uses clinical unstructured text as input.We propose an integrated learning approach based on mul-tiple pre-training language models to construct an end-to-end model for the prediction of TCM syndromes.The experimental results show that the method is effective in the prediction of TCM syndromes,with model evaluation metrics of macro-average F1 and Accuracy of 90.97%and 94.23%respectively.Compared with the main text classi-fication models in the literature,our method has significant advantages in optimizing clinical diagnosis decision-making.Second,for the task of clinical treatment decision-making,Chapter 4 of the thesis studies the automatic generation of TCM prescription based on sequence to sequence learning,which solves the problem of how to assist TCM to formulate prescription.Our study models the automatic generation of TCM prescription as a sequence-to-sequence text generation problem.We propose a method to fine-tune the pre-training language model using a special masking mechanism,which enables the pre-training language model without the mechanism of sequence generation to complete the prescription gen-eration,We also model the word level using the full name encoding of herbs,which further improves the semantic representation of the model.The experimental results show that the method achieves good performance in the generation of high-frequency Chinese medicine,with the model evaluation metrics Recall and Precision of 78.24%and 70.60%respectively.And the prescription generation of our model significantly outperforms that of models in related literature.Finally,for the task of herbal efficacy evaluation,Chapter 5 of the thesis examines the correlation between Chinese herbal medicines and the treatment efficacy based on association rules,which solves the problem of how to assist clinical TCM analysis of herbal efficacy.Our study proposes a learning framework based on association rules for the correlation between Chinese herbal medicines and the treatment efficacy.Firstly,we respectively define whether a single laboratory index is effectively improved and whether the treatment overall outcome of patients are effectively improved.Then,we use association rule learning to mine the association patterns between Chinese herbal medicines and effectively improved indexes or the treatment overall outcome.Finally,we use chi-square test to extract statistically significant association rules.We mine the rules from four aspects:single herb-single index,single herb-multi indexes,multi herbs-single index and multi herbs-multi indexes.And we compare the mining results under different evaluation methods of treatment efficacy.Our experimental results have been verified by clinicians and related literature,indicating the effectiveness and reliability of the method.The main contributions of this thesis are as follows:(1)Our work mines multiple types of data from TCM electronic medical records,especially the unstructured text.Through the study of three important problems,i.e.the diagnosis of TCM syndrome,the automatic generation of the prescription and the evaluation TCM efficacy,the research on decision support of TCM clinical diagnosis and treatment is further supplemented.(2)Our work provides a new research method for mining the TCM electronic medical records.For the prediction of TCM syndrome and the generation of prescription,we propose a method of using pre-training language model to train in small-scale data,and establishe an end-to-end model based on deep learning.For the evaluation TCM ef-ficacy,a method combining association rules and chi-square test is proposed to mine statistically significant association rules among high-dimensional sparse variables.(3)Our work has a positive significance to improve the quality of clinical diagnosis and treatment decision-making of traditional Chinese medicine.Through the construction of TCM clinical decision-making system from diagnosis,medication to efficacy evalu-ation,it can effectively meet the actual clinical needs.On the one hand,it can provide a useful reference for doctors to make diagnosis and treatment decisions,reduce the bur-den of doctors and improve the level of diagnosis and treatment.On the other hand,it helps to enhance the accuracy and pertinence of patients' diagnosis and treatment,so as to improve the treatment effect of patients and reduce the medical expenses of patients.