Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates o...Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs.The pathological mechanisms and multiple factors involved in CVDs are complex;thus,traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks,medical image interpretations,therapeutic decision-making,and disease prognosis prediction.Meanwhile,traditional Chinese medicine(TCM)has been widely used for treating CVDs.TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs.Big data have been generated to investigate the scientific basis of TCM diagnostic methods.TCM formulae contain multiple herbal items.Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability.Recent progress in artificial intelligence(AI)technology has allowed these challenges to be resolved,which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae.Herein,we briefly introduce the basic concept and current progress of AI and machine learning(ML)technology,and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs.Furthermore,we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs.We expect the application of AI and ML technology to promote synergy between western medicine and TCM,which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.展开更多
AIM:To investigate the prevalence and physicians' detection rate of depressive and anxiety disorders in gastrointestinal(GI)outpatients across China. METHODS:A hospital-based cross-sectional survey was conducted i...AIM:To investigate the prevalence and physicians' detection rate of depressive and anxiety disorders in gastrointestinal(GI)outpatients across China. METHODS:A hospital-based cross-sectional survey was conducted in the GI outpatient departments of 13 general hospitals.A total of 1995 GI outpatients were recruited and screened with the Hospital Anxiety and Depression Scale(HADS).The physicians of the GI departments performed routine clinical diagnosis and management without knowing the HADS score results.Subjects with HADS scores≥8 were subsequently interviewed by psychiatrists using the Mini International Neuropsy-chiatric Interview(MINI)to make further diagnoses. RESULTS:There were 1059 patients with HADS score ≥8 and 674(63.64%)of them undertook the MINI interview by psychiatrists.Based on the criteria of Diagnostic and Statistical Manual of Mental Disorders (4th edition),the adjusted current prevalence for depressive disorders,anxiety disorders,and comorbidity of both disorders in the GI outpatients was 14.39%, 9.42%and 4.66%,respectively.Prevalence of depressive disorders with suicidal problems[suicide attempt or suicide-related ideation prior or current;module C (suicide)of MINI score≥1]was 5.84%in women and 1.64%in men.The GI physicians'detection rate of depressive and anxiety disorders accounted for 4.14%. CONCLUSION:While the prevalence of depressive and anxiety disorders is high in Chinese GI outpatients, the detection rate of depressive and anxiety disorders by physicians is low.展开更多
Objective To optimize therapeutic regimens for gastro-esophageal reflux disease(GERD),artificial neural networks(ANNs)are used to simulate and set up an intelligent traditional Chinese medicine(TCM)treatment system.Me...Objective To optimize therapeutic regimens for gastro-esophageal reflux disease(GERD),artificial neural networks(ANNs)are used to simulate and set up an intelligent traditional Chinese medicine(TCM)treatment system.Methods ANNs were employed for machine learning;the clinical syndrome differentiation and treatment determination were simulated through systematic learning of therapeutic regimens for GERD symptoms in the ancient literature;and case simulation was conducted to achieve objective verification.Results The conformity of machinery prescription with the ancient literature exceeded95%.Conclusion The application of machine learning to TCM intelligent prescription is feasible and worthy of further study.展开更多
基金The Health and Medical Research Fund,Hong Kong(17181811)。
文摘Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs.The pathological mechanisms and multiple factors involved in CVDs are complex;thus,traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks,medical image interpretations,therapeutic decision-making,and disease prognosis prediction.Meanwhile,traditional Chinese medicine(TCM)has been widely used for treating CVDs.TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs.Big data have been generated to investigate the scientific basis of TCM diagnostic methods.TCM formulae contain multiple herbal items.Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability.Recent progress in artificial intelligence(AI)technology has allowed these challenges to be resolved,which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae.Herein,we briefly introduce the basic concept and current progress of AI and machine learning(ML)technology,and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs.Furthermore,we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs.We expect the application of AI and ML technology to promote synergy between western medicine and TCM,which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.
基金Supported by The former Wyeth Pharmaceutical Co.,Ltd., Madison,NJ,United States
文摘AIM:To investigate the prevalence and physicians' detection rate of depressive and anxiety disorders in gastrointestinal(GI)outpatients across China. METHODS:A hospital-based cross-sectional survey was conducted in the GI outpatient departments of 13 general hospitals.A total of 1995 GI outpatients were recruited and screened with the Hospital Anxiety and Depression Scale(HADS).The physicians of the GI departments performed routine clinical diagnosis and management without knowing the HADS score results.Subjects with HADS scores≥8 were subsequently interviewed by psychiatrists using the Mini International Neuropsy-chiatric Interview(MINI)to make further diagnoses. RESULTS:There were 1059 patients with HADS score ≥8 and 674(63.64%)of them undertook the MINI interview by psychiatrists.Based on the criteria of Diagnostic and Statistical Manual of Mental Disorders (4th edition),the adjusted current prevalence for depressive disorders,anxiety disorders,and comorbidity of both disorders in the GI outpatients was 14.39%, 9.42%and 4.66%,respectively.Prevalence of depressive disorders with suicidal problems[suicide attempt or suicide-related ideation prior or current;module C (suicide)of MINI score≥1]was 5.84%in women and 1.64%in men.The GI physicians'detection rate of depressive and anxiety disorders accounted for 4.14%. CONCLUSION:While the prevalence of depressive and anxiety disorders is high in Chinese GI outpatients, the detection rate of depressive and anxiety disorders by physicians is low.
文摘Objective To optimize therapeutic regimens for gastro-esophageal reflux disease(GERD),artificial neural networks(ANNs)are used to simulate and set up an intelligent traditional Chinese medicine(TCM)treatment system.Methods ANNs were employed for machine learning;the clinical syndrome differentiation and treatment determination were simulated through systematic learning of therapeutic regimens for GERD symptoms in the ancient literature;and case simulation was conducted to achieve objective verification.Results The conformity of machinery prescription with the ancient literature exceeded95%.Conclusion The application of machine learning to TCM intelligent prescription is feasible and worthy of further study.
