Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome

As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of knowledge.The diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing technology.This paper proposes a knowledge-based syndrome reasoning method in computer-assisted diagnosis.This 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 path.According 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 causes.Moreover,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome calculation.Finally,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.

Application of Big Data Deep Learning in Auxiliary Diagnosis of Lower Extremity Arteriosclerosis Obliterans

At present,the incidence rate of arteriosclerosis obliterans(LEASO)of the lower extremities is significantly increased by aging and lifestyle changes.It 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 intelligence.Medical institutions at all levels can produce a large number of lower limb vascular image data every day.Using 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.

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

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.

Auxiliary diagnostic system for ADHD in children based on AI technology

Traditional diagnosis of attention deficit hyperactivity disorder(ADHD)in children is primarily through a questionnaire filled out by parents/teachers and clinical observations by doctors.It is inefficient and heavily depends on the doctor’s level of experience.In this paper,we integrate artificial intelligence(AI)technology into a software-hardware coordinated system to make ADHD diagnosis more efficient.Together 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 test.Then,a multi-modal deep learning model is proposed to classify abnormal behavior fragments of children from the captured videos.In combination with other system modules,standardized diagnostic reports can be automatically generated,including test results,abnormal behavior analysis,diagnostic aid conclusions,and treatment recommendations.This 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.