On standardization of basic datasets of electronic medical records in traditional Chinese medicine

2019 ◽  
Vol 174 ◽  
pp. 65-70
Author(s):  
Hong Zhang ◽  
Wandong Ni ◽  
Jing Li ◽  
Youlin Jiang ◽  
Kunjing Liu ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Electronic Medical Records ◽  
Medical Records

Transformer + GAN based Traditional Chinese Medicine inpatient prescription recommendation (Preprint)

2021 ◽  
Author(s):  
Hong Zhang
Keyword(s):  
Decision Making ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Electronic Medical Records ◽  
Clinical Diagnosis ◽  
Medical Records ◽  
Training Data ◽  
Diagnosis And Treatment ◽  
Test Results ◽  
Treatment Scheme

BACKGROUND Clinical diagnosis and treatment decision making support is at the core of medical artificial intelligent research, in which Traditional Chinese Medicine (TCM) decision making is an important part. Traditional Chinese Medicine is a traditional medical system originated from China, of which the main clinical model is to conduct individualized diagnosis and treatment by relying on the four-diagnosis information. One of the key tasks of the TCM artificial intelligence research is to develop techniques and methods of clinical prescription decision making which takes all the relevant information of a patient as input, and produces a diagnosis and treatment scheme as output. Given the complexity of TCM clinical diagnosis and treatment schemes, decision making support of clinical diagnosis and treatment schemes remains as a research challenge for lacking of an effective solution. Fortunately, as the volume of the massive clinical data in the form of electronic medical records increases rapidly, it becomes possible for the computer to produce personalized diagnosis and treatment scheme recommendation through machine learning on the basis of the clinical big data. OBJECTIVE The objective of this research is to develop a real-time diagnosis and treatment scheme recommendation model for TCM inpatients. This is accomplished by using historical clinical medical records as training data to train a Transformer network. Furthermore, to alleviate the issue of overfitting, a Generative Adversarial Network is used to generate noise-added samples from the original training data. These noise-added samples along with the original samples form the complete train data set. METHODS valid information, such as the patient’s current sickness situation, medicines taken, nursing care given, vital signs, examinations and test results, is extracted from the patient’s electronic medical records, then the obtained information is sorted chronically, to produce a sequence of data of each patient. These time-sequence data is then used as input to the Transformer network. The output of the network would be the prescription information a physician would give. Overfitting is a common problem in machine learning, and becomes especially server when the network is complex with insufficient training data. In this research, a Generative Adversarial Network, is used to double the number of training samples by producing noise-added samples from the original samples. This, to a great extent, lessens the overfitting problem. RESULTS A total of 21,295 copies of inpatient electronic medical records from Guang’anmen traditional Chinese medicine hospital was used in this research. These records were created between January 2017 and December 2018, covering a total of 6352 kinds of medicines. These medicines were sorted into 829 types of first category medicines based on the class relationships among medicines. As shown by the test results, the performance of a fully trained Transformer model can have an average precision rate of 80.58%，and an average recall rate of 68.49%. CONCLUSIONS As shown by the preliminary test results, the Transformer-based TCM prescription recommendation model outperforms the existing conventional methods. The extra training samples generated by the GAN network helps to overcome the overfitting issue, leading a further improved recall rate and precision rate.

scholarly journals Research on Standard Compliance Test Algorithm Based on Electronic Medical Records of Traditional Chinese Medicine Outpatients

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Li He ◽  
Zi Yi Zhou ◽  
Fu Sheng Niu ◽  
Yu Fan Yang ◽  
Qiang Xu ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Information Platform ◽  
Related Information ◽  
Digital Medicine ◽  
Compliance Testing ◽  
Test Algorithm ◽  
Algorithm Scheme

Objectives. To effectively evaluate the compliance degree between the electronic medical records of Traditional Chinese Medicine (TCM) hospitals, as well as the information platform, and the related information standards of electronic medical records, a standard compliance testing scheme based on electronic medical records of TCM outpatients is proposed. Methods. This research selected the data of clinical outpatients accumulated in 10 years by the Digital Medicine Institute of Chengdu University of TCM and processed the data through security check and desensitization process. And then 28348 cases of processed electronic medical records of TCM outpatients were inputted into the standard compliance testing platform for assessment. The result was then outputted. Results. There are 924 cases among the 28348 that can be rated as five-star medical records, 84 cases four-star, 132 cases three-star, 12460 cases two-star, 13488 one-star, and 1260 cases zero-star through the integrity and standardization test. Conclusion. By the way of assessing the integrity and standardization of data, the standard compliance test algorithm scheme for electronic medical records of TCM outpatients introduced in this paper can solve the problems such as data unavailability caused by ununified codes and incomplete data in the data-sharing process and provides technical support for the construction of data standardization testing in electronic medical records of TCM outpatients.

Exploration of Intelligent Development of Medical Heritage

2021 ◽  
Vol 105 ◽  
pp. 272-281
Author(s):  
Jing Hua Li ◽  
Ying Hui Wang ◽  
Zong You Li ◽  
Qi Yu ◽  
Ye Tian ◽  
...  
Keyword(s):  
Big Data ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Medical Records ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Information Barriers ◽  
Deep Learning Algorithm ◽  
Application Systems ◽  
Analysis Platform

With the rapid development of science and technology, more and more new methods and technologies have been added to the traditional Chinese Medicine Inheritance model, which makes the process of inheritance of famous doctors have more means, and the results of inheritance are more objective, rigorous and intelligent. In the process of inheriting the informationization of famous doctors, there are some bottlenecks, such as data acquisition difficulties, data processing difficulties, algorithm application difficulties, analysis and summary difficulties. Integration of artificial intelligence with big data, deep learning algorithm and knowledge atlas technology has brought technological innovation to the informationization of famous doctors' inheritance. Under this wave, the team of the Intelligent Research and Development Center of Traditional Chinese Medicine, Institute of Traditional Chinese Medicine Information, Chinese Academy of Traditional Chinese Medical Sciences, has developed a series of professional application systems in the field of traditional Chinese medicine around the planning of famous doctors' inheritance and excavation, and has developed ancient Chinese medicine, such as Today's Medical Records Cloud Platform, Medical Records Big Data Analysis Platform, Cloud Medical Records APP, Famous Medical Heritage Workstation. To a certain extent, it can solve the problems of inefficient collection of medical records, lack of objective data support and information barriers in the summary of famous doctors' experience under the limitation of traditional model, so as to promote the inheritance of famous doctors' experience and enhance the teaching ability and efficiency of teachers and apprentices.

Traditional Chinese Medicine Formulation Therapy in the Treatment of Coronavirus Disease 2019 (COVID-19)

2020 ◽  
Vol 48 (07) ◽  
pp. 1523-1538
Author(s):  
Jia Shi ◽  
Yunfei Lu ◽  
Yuan Zhang ◽  
Lu Xia ◽  
Chen Ye ◽  
...  
Keyword(s):  
Risk Factor ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Medical Records ◽  
Early Stage ◽  
Logistic Models ◽  
Oral Swab ◽  
Blood Samples ◽  
Traditional Chinese Medicine Formulation ◽  
Negative Conversion

This study aimed to investigate the efficacy of Traditional Chinese Medicine (TCM) decoction with different intervention timepoints in the treatment of coronavirus disease 2019 (COVID-19) patients. We retrospectively collected the medical records and evaluated the outcomes of COVID-19 patients that received TCM decoction treatment at different timepoints. A total of 234 COVID-19 patients were included in this study. Patients who received TCM decoction therapy within 3 days or 7 days after admission could achieve shorter hospitalization days and disease periods compared to those who received TCM decoction [Formula: see text] 7 days after admission (all [Formula: see text]). Patients who received TCM decoction therapy within 3 days had significantly fewer days to negative SARS-CoV-2 from nasopharyngeal/oral swab and days to negative SARS-CoV-2 from urine/stool/blood samples compared to those received TCM decoction [Formula: see text] days after admission (all [Formula: see text]). Patients who received TCM decoction therapy on the 3rd to 7th day after admission had a faster achievement of negative SARS-CoV-2 from urine/stool/blood samples compared to those who received TCM decoction [Formula: see text] days after admission ([Formula: see text]). Logistic models revealed that more days from TCM decoction to admission [Formula: see text] days might be a risk factor for long hospitalization days, disease period, and slower negative-conversion of SARS-CoV-2 (all [Formula: see text]). Conclusively, our results suggest that TCM decoction therapy should be considered at the early stage of COVID-19 patients.

scholarly journals SDTM: A Novel Topic Model Framework for Syndrome Differentiation in Traditional Chinese Medicine

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Jialin Ma ◽  
Xiaoqiang Gong ◽  
Zhaojun Wang ◽  
Qian Xie
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Medical Records ◽  
Bayesian Method ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Syndrome Differentiation ◽  
Semantic Relationship ◽  
Model Framework ◽  
Intelligent Methods

Syndrome differentiation is the most basic diagnostic method in traditional Chinese medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity, and vagueness. Recently, artificial intelligent methods have been introduced to discover the regularities of syndrome differentiation from TCM medical records, but the existing DM algorithms failed to consider how a syndrome is generated according to TCM theories. In this paper, we propose a novel topic model framework named syndrome differentiation topic model (SDTM) to dynamically characterize the process of syndrome differentiation. The SDTM framework utilizes latent Dirichlet allocation (LDA) to discover the latent semantic relationship between symptoms and syndromes in mass of Chinese medical records. We also use similarity measurement method to make the uninterpretable topics correspond with the labeled syndromes. Finally, Bayesian method is used in the final differentiated syndromes. Experimental results show the superiority of SDTM over existing topic models for the task of syndrome differentiation.

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