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auxiliary diagnosis Based on the Knowledge Graph of TCM Syndrome

Computers, Materials & Continua 2020年第10期65卷 481-494页

作者： Yonghong Xie Liangyuan Hu Xingxing Chen Jim Feng Dezheng Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Amphenol Global Interconnect Systems San JoseCA 95131USA

As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of *** diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing *** paper proposes a knowledge-based syndrome reasoning method in computer-assisted *** method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning *** to this reasoning path,we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and ***,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome ***,combined with symptoms,syndrome,and causes,the disease could be confirmed comprehensively by voting,and the experiment shows that the system can help doctors and families to disease diagnosis effectively.

关键词： Knowledge graph reinforcement learning auxiliary diagnosis inference path

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Application of Big Data Deep Learning in auxiliary diagnosis of Lower Extremity Arteriosclerosis Obliterans

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Journal of Clinical and Nursing Research 2021年第5期5卷 108-112页

作者： Linbo Liu Yang Liu Hongjun Wang Yi Zhang Zhijie Liao Shengdong Du The Third Hospital of Mianyang Sichuan Mental Health CenterMianyang 621000Sichuan ProvinceChina Affiliated Hospital of Chengdu University of Traditional Chinese Medicine Chengdu 610072Sichuan ProvinceChina School of Computing and Artificial Intelligence Southwestjiaotong UniversityChengdu 611756Sichuan ProvinceChina

At present,the incidence rate of arteriosclerosis obliterans(LEASO)of the lower extremities is significantly increased by aging and lifestyle *** is of great importance to predict the LEASO effectively and accurately by analyzing the imaging data of the lower extremities⑴.At this stage,China has entered the era of big data and artificial *** institutions at all levels can produce a large number of lower limb vascular image data every *** big data deep learning technology to intelligently analyze a large number of image data,and then carry out auxiliary diagnosis,so as to improve the diagnosis and treatment effect of LEASO is the focus of clinical research.

关键词： Big data Deep learning Arteriosclerosis obliterans of lower limbs auxiliary diagnosis

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Clinical Application of Sex Hormone in Different Physiological Periods in the diagnosis of Infertility Patients

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Natural Science 2024年第6期16卷 102-110页

作者： Jianhong Nong Daxian Tan Arshad Mehmood Tingchao Wang Xin Liu Li Deng Bowen Wei Department of Clinical Laboratory Reproductive Hospital of Guangxi Zhuang Autonomous Region Nanning China Department of Andrology Reproductive Hospital of Guangxi Zhuang Autonomous Region Nanning China Department of Neurology The Second Hospital of Hebei Medical University Shijiazhuang China

Background: Infertility is characterized by the inability to conceive after a year of regular unprotected intercourse. Aims: This study aimed to investigate the diagnostic value of sex hormone levels during different physiological periods in the diagnosis of infertility patients. Methods: From December 2019 to May 2021, a total of 93 infertility patients were admitted and selected as the observation group. Among them, 31 cases were in the follicular stage, 31 cases in the ovulation stage, and 31 cases in the luteal stage. Ninety-three healthy women for fertility evaluation due to male infertility were selected as the control group. The control group included 31 women in the follicular phase, 31 women in the ovulatory phase, and 31 women in the luteal phase. The levels of sex hormones (prolactin (PRL), luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), testosterone (T), and progesterone (P)) during different physiological phases were compared between the observation and control groups. Results: The follicular phase showed no significant difference in LH levels between the observation group and the control group. The observation group showed higher levels of PRL and P compared to the control group, while the levels of FSH, E2, and T were lower in the observation group compared to the control group. The ovulation phase showed no significant difference in PRL levels between the two groups. The observation group showed lower levels of LH, FSH, E2, T, and P compared to the control group. The luteal phase showed no statistical difference in E2 levels between the two groups. The observation group showed higher levels of PRL, LH, and FSH compared to the control group, while the levels of T and P were lower in the observation group compared to the control group. Conclusion: Infertile women show variations in hormone levels compared to the normal levels during the follicular phase, ovulatory phase, and luteal phase.

关键词： Different Periods Sex Hormone Level Detection Infertility auxiliary diagnosis

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auxiliary diagnostic system for ADHD in children based on AI technology

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Frontiers of Information Technology & Electronic Engineering 2021年第3期22卷 400-414页

作者： Yanyi ZHANG Ming KONG Tianqi ZHAO Wenchen HONG Di XIE Chunmao WANG Rongwang YANG Rong LI Qiang ZHU Department of Psychology The Children’s HospitalZhejiang University School of MedicineNational Clinical Research Center for Child HealthHangzhou 310052China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Hikvision Research Institute Hangzhou 310052China

Traditional diagnosis of attention deficit hyperactivity disorder(ADHD)in children is primarily through a questionnaire filled out by parents/teachers and clinical observations by *** is inefficient and heavily depends on the doctor’s level of *** this paper,we integrate artificial intelligence(AI)technology into a software-hardware coordinated system to make ADHD diagnosis more *** with the intelligent analysis module,the camera group will collect the eye focus,facial expression,3D body posture,and other children’s information during the completion of the functional ***,a multi-modal deep learning model is proposed to classify abnormal behavior fragments of children from the captured *** combination with other system modules,standardized diagnostic reports can be automatically generated,including test results,abnormal behavior analysis,diagnostic aid conclusions,and treatment *** system has participated in clinical diagnosis in Department of Psychology,The Children’s Hospital,Zhejiang University School of Medicine,and has been accepted and praised by doctors and patients.

关键词： Attention deficit hyperactivity disorder(ADHD) auxiliary diagnosis Computer vision Deep learning BERT

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