Coronavirus disease 2019(COVID-19)has become a worldwide pandemic.Hospitalized patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinician...Coronavirus disease 2019(COVID-19)has become a worldwide pandemic.Hospitalized patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients.Here,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death.The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all cohorts.Moreover,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of 0.9551.In summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.展开更多
Atrial fibrillation(AF)is the most common arrhythmia diagnosed in clinical practice.The consequences of AF have been clearly estab-lished in multiple large observational cohort studies and include increased stroke and...Atrial fibrillation(AF)is the most common arrhythmia diagnosed in clinical practice.The consequences of AF have been clearly estab-lished in multiple large observational cohort studies and include increased stroke and systemic embolism rates if no oral anticoagulation is prescribed,with increased morbidity and mortality.With the worldwide aging of the population characterized by a large influx of"baby boomers"with or without risk factors for developing AF,an epidemic is forecasted within the next 10 to 20 years.Although not all studies support this evidence,it is clear that AF is on the rise and a significant amount of health resources are invested in detecting and managing AF This review focuses on the worldwide burden of AF and reviews global health strategies focused on improving detection,prevention and risk stratification of AF,recently recommended by the World Heart Federation.展开更多
Objective:The aim of this study is to construct a curated bibliographic dataset for a landscape analysis on Health Artiffcial Intelligence(HAI)research.Data Source:We integrated HAI-related bibliographic records,inclu...Objective:The aim of this study is to construct a curated bibliographic dataset for a landscape analysis on Health Artiffcial Intelligence(HAI)research.Data Source:We integrated HAI-related bibliographic records,including publications,open research datasets,patents,research grants,and clinical trials from Medline and Dimensions.Methods:Searching:Relevant documents were identiffed using Medical Subject Headings(MeSH)and Field of Research(FoR)indexed by 2 bibliographic databases,Medline and Dimensions.Extracting:MeSH terms annotated from the aforementioned bibliographic databases served as the primary information for our processing.For document records lacking MeSH terms,we reextracted them using the Medical Text Indexer(MTI).Mapping:In order to enhance interoperability,HAI multi-documents were organized using a mapping system incorporating MeSH,FoR,The International Classiffcation of Diseases(ICD-10),and Systematized Nomenclature of Medicine Clinical Terms(SNOMED CT).Integrating:All documents were curated based on a pre-deffned ontology of health problems and AI technologies from the MeSH hierarchy.Results:We collected 96,332 HAI documents(publications:75,820,open research datasets:638,patents:11,226,grants:6,113,and clinical trials:2,535)during 2009 to 2021.On average,75.12%of the documents were tagged with at least one label related to either health problems or AI technologies(with 92.9%of publications tagged).Summary:This study presents a comprehensive pipeline for processing and curating HAI bibliographic documents following the FAIR(Findable,Accessible,Interoperable,Reusable)standard,offering a valuable multidimensional collection for the community.This dataset serves as a crucial resource for horizontally scanning the funding,research,clinical assessments,and innovations within the HAI ffeld.展开更多
基金supported by the Special Fund for Novel Coronavirus Pneumonia from the Department of Science and Technology of Hubei Province(2020FCA035)the Fundamental Research Funds for the Central Universities,Huazhong University of Science and Technology(2020kfyXGYJ023).
文摘Coronavirus disease 2019(COVID-19)has become a worldwide pandemic.Hospitalized patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients.Here,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death.The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all cohorts.Moreover,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of 0.9551.In summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.
文摘Atrial fibrillation(AF)is the most common arrhythmia diagnosed in clinical practice.The consequences of AF have been clearly estab-lished in multiple large observational cohort studies and include increased stroke and systemic embolism rates if no oral anticoagulation is prescribed,with increased morbidity and mortality.With the worldwide aging of the population characterized by a large influx of"baby boomers"with or without risk factors for developing AF,an epidemic is forecasted within the next 10 to 20 years.Although not all studies support this evidence,it is clear that AF is on the rise and a significant amount of health resources are invested in detecting and managing AF This review focuses on the worldwide burden of AF and reviews global health strategies focused on improving detection,prevention and risk stratification of AF,recently recommended by the World Heart Federation.
基金funded by the National Key R&D Program for Young Scientists(2022YFF0712000).
文摘Objective:The aim of this study is to construct a curated bibliographic dataset for a landscape analysis on Health Artiffcial Intelligence(HAI)research.Data Source:We integrated HAI-related bibliographic records,including publications,open research datasets,patents,research grants,and clinical trials from Medline and Dimensions.Methods:Searching:Relevant documents were identiffed using Medical Subject Headings(MeSH)and Field of Research(FoR)indexed by 2 bibliographic databases,Medline and Dimensions.Extracting:MeSH terms annotated from the aforementioned bibliographic databases served as the primary information for our processing.For document records lacking MeSH terms,we reextracted them using the Medical Text Indexer(MTI).Mapping:In order to enhance interoperability,HAI multi-documents were organized using a mapping system incorporating MeSH,FoR,The International Classiffcation of Diseases(ICD-10),and Systematized Nomenclature of Medicine Clinical Terms(SNOMED CT).Integrating:All documents were curated based on a pre-deffned ontology of health problems and AI technologies from the MeSH hierarchy.Results:We collected 96,332 HAI documents(publications:75,820,open research datasets:638,patents:11,226,grants:6,113,and clinical trials:2,535)during 2009 to 2021.On average,75.12%of the documents were tagged with at least one label related to either health problems or AI technologies(with 92.9%of publications tagged).Summary:This study presents a comprehensive pipeline for processing and curating HAI bibliographic documents following the FAIR(Findable,Accessible,Interoperable,Reusable)standard,offering a valuable multidimensional collection for the community.This dataset serves as a crucial resource for horizontally scanning the funding,research,clinical assessments,and innovations within the HAI ffeld.
