The ability to identify patients with risk of mortality in the initial stages allows us to introduce a more aggressive treatment in order to improve patients’ survival. In this study, we used systemic inflammatory re...The ability to identify patients with risk of mortality in the initial stages allows us to introduce a more aggressive treatment in order to improve patients’ survival. In this study, we used systemic inflammatory response syndrome (SIRS) criteria, respiratory and heart rate per minute, and consciousness level [(Glasgow coma scale (GCS)] to develop a formula to predict death in patients admitted to the Infectious Diseases ward of Imam Reza hospital. Methods: This descriptive study was a cross sectional study done in the Infectious Diseases ward of Imam Reza hospital, Mashhad University of Medical Sciences, Iran. Alive and dead patients between the dates September 1, 2006 to September 1, 2007 were studied. In this study, data such as past medical history, prescribed drugs and their administration by nursing and medical staff was extracted from patients’ files. Also, the time of death, the first vital signs recorded in the hospital and the formula H = (PR + RR) - GCS (respiratory rate per minute plus heart rate per minute minus Glasgow coma scale (GCS)) was calculated for both alive and dead patients. Data was analyzed by SPSS software. Mann-Whitney test, Roc Curve, and logistic regression model were used for data analysis. Results: The total number of admitted patients was 1007 of whom 90 (10.82%) died. One patient was excluded from the study. Out of 90 dead patients, 51 (56.6%) were male and 39 (43.3%) were female. There was no significant difference between the two groups regarding the gender (P > 0.05). The mean age of the study group (deceased) was 59 ± 21 and the mean age of the control group (alive) was 58 ± 21. The Mann-Whitney test showed that the result of H Formula was significantly different between the two groups, (126 ± 26 for the study group and 111 ± 22 for the control group). The cutoff for H Formula was equal to 112.5. Negative and positive predictive values, specificity and sensitivity were 0.85, 0.35, 0.57, and 0.70 respectively. Logistic regression results show that the H index contents independently affected the mortality of infected patients. Conclusion: With regard to the importance of measuring vital signs in diagnosis and determining the mortality in patients with infectious disease, the H (Heydari) formula can be valuable for evaluation and determination of mortality risk and consequently, early intervention. Patients with severe tachycardia, severe tachypnea and altered mental status that cannot be properly and quickly improved within 2 hours after admission via hydration and other measures are at higher risk of mortality.展开更多
Objective:China is one of the countries with the heaviest burden of gastric cancer(GC)in the world.Understanding the epidemiological trends and patterns of GC in China can contribute to formulating effective preventio...Objective:China is one of the countries with the heaviest burden of gastric cancer(GC)in the world.Understanding the epidemiological trends and patterns of GC in China can contribute to formulating effective prevention strategies.Methods:The data on incidence,mortality,and disability-adjusted life-years(DALYs)of GC in China from1990 to 2019 were obtained from the Global Burden of Disease Study(2019).The estimated annual percentage change(EAPC)was calculated to evaluate the temporal trends of disease burden of GC,and the package Nordpred in the R program was used to perform an age-period-cohort analysis to predict the numbers and rates of incidence and mortality in the next 25 years.Results:The number of incident cases of GC increased from 317.34 thousand in 1990 to 612.82 thousand in2019,while the age-standardized incidence rate(ASIR)of GC decreased from 37.56 per 100,000 in 1990 to 30.64 per 100,000 in 2019,with an EAPC of-0.41[95%confidence interval(95%CI):-0.77,-0.06].Pronounced temporal trends in mortality and DALYs of GC were observed.In the next 25 years,the numbers of new GC cases and deaths are expected to increase to 738.79 thousand and 454.80 thousand,respectively,while the rates of incidence and deaths should steadily decrease.The deaths and DALYs attributable to smoking were different for males and females.Conclusions:In China,despite the fact that the rates of GC have decreased during the past three decades,the numbers of new GC cases and deaths increased,and will continue to increase in the next 25 years.Additional strategies are needed to reduce the burden of GC,such as screening and early detection,novel treatments,and the prevention of risk factors.展开更多
The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in o...The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness.We present a first step towards building an artificial intelligence(AI)framework,with predictive analytics(PA)capabilities applied to real patient data,to provide rapid clinical decision-making support.COVID-19 has presented a pressing need as a)clinicians are still developing clinical acumen given the disease’s novelty,and b)resource limitations in a rapidly expanding pandemic require difficult decisions relating to resource allocation.The objectives of this research are:(1)to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes,and(2)to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation.The predictive models learn from historical data to help predict specifically who will develop acute respiratory distress syndrome(ARDS),a severe outcome in COVID-19.Our experimental results based on two hospitals in Wenzhou,Zhejang,China identify features most predictive of ARDS in COVID-19 initial presentation which would not have stood out to clinicians.A mild increase in elevated alanine aminotransferase(ALT)(a liver enzyme)),a presence of myalgias(body aches),and an increase in hemoglobin,in this order,are the clinical features,on presentation,that are the most predictive.Those two centers’COVID-19 case series symptoms on initial presentation can help predict severe outcomes.Predictive models that learned from historical data of patients from two Chinese hospitals achieved 70%to 80%accuracy in predicting severe cases.展开更多
Background Climate change presents an imminent threat to almost all biological systems across the globe.In recent years there have been a series of studies showing how changes in climate can impact infectious disease ...Background Climate change presents an imminent threat to almost all biological systems across the globe.In recent years there have been a series of studies showing how changes in climate can impact infectious disease transmission.Many of these publications focus on simulations based on in silico data,shadowing empirical research based on feld and laboratory data.A synthesis work of empirical climate change and infectious disease research is still lacking.Methods We conducted a systemic review of research from 2015 to 2020 period on climate change and infectious diseases to identify major trends and current gaps of research.Literature was sourced from Web of Science and PubMed literary repositories using a key word search,and was reviewed using a delineated inclusion criteria by a team of reviewers.Results Our review revealed that both taxonomic and geographic biases are present in climate and infectious disease research,specifcally with regard to types of disease transmission and localities studied.Empirical investigations on vector-borne diseases associated with mosquitoes comprised the majority of research on the climate change and infectious disease literature.Furthermore,demographic trends in the institutions and individuals published revealed research bias towards research conducted across temperate,high-income countries.We also identifed key trends in funding sources for most resent literature and a discrepancy in the gender identities of publishing authors which may refect current systemic inequities in the scientifc feld.Conclusions Future research lines on climate change and infectious diseases should considered diseases of direct transmission(non-vector-borne)and more research efort in the tropics.Inclusion of local research in low-and middle-income countries was generally neglected.Research on climate change and infectious disease has failed to be socially inclusive,geographically balanced,and broad in terms of the disease systems studied,limiting our capacities to better understand the actual efects of climate change on health.展开更多
Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of th...Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.展开更多
目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发...目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发展历程及研究方向进行梳理,分析当前主要问题与挑战,总结常见预测模型及其优化方向。结果/结论互联网传染病监测研究呈监测疾病多样化、数据来源精细化和专业化等趋势。由于互联网数据的复杂性和不确定性,现有模型大多仅适用于短时或实时预测。通过构建组合模型、加强多源数据融合、完善关键词与影响因素选择等方式,可进一步优化模型,加强拟合效果和预测能力。展开更多
The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-s...The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.展开更多
以Web of Science数据库1945年以来的英文文献为依据,使用文献计量方法分析传染病史研究的文献数量和国家地区分布特征,描绘国际传染病史研究的知识图景,发现该领域研究热点集中在全球史视野的传染病史、公共卫生道德标准、新发和再发...以Web of Science数据库1945年以来的英文文献为依据,使用文献计量方法分析传染病史研究的文献数量和国家地区分布特征,描绘国际传染病史研究的知识图景,发现该领域研究热点集中在全球史视野的传染病史、公共卫生道德标准、新发和再发传染病、传染病起源、跨学科视角分析五个方面。从发展趋势看,新发和再发传染病的机制已被普遍接受,传染病中的道德伦理问题研究不足,目前“根除主义”仍存在争议,跨学科研究依然具有吸引力;从国际传染病史文献的发表趋势来看,传染病史研究正处于繁荣发展期;传染病史研究文献在其他领域被广泛引用。展开更多
文摘The ability to identify patients with risk of mortality in the initial stages allows us to introduce a more aggressive treatment in order to improve patients’ survival. In this study, we used systemic inflammatory response syndrome (SIRS) criteria, respiratory and heart rate per minute, and consciousness level [(Glasgow coma scale (GCS)] to develop a formula to predict death in patients admitted to the Infectious Diseases ward of Imam Reza hospital. Methods: This descriptive study was a cross sectional study done in the Infectious Diseases ward of Imam Reza hospital, Mashhad University of Medical Sciences, Iran. Alive and dead patients between the dates September 1, 2006 to September 1, 2007 were studied. In this study, data such as past medical history, prescribed drugs and their administration by nursing and medical staff was extracted from patients’ files. Also, the time of death, the first vital signs recorded in the hospital and the formula H = (PR + RR) - GCS (respiratory rate per minute plus heart rate per minute minus Glasgow coma scale (GCS)) was calculated for both alive and dead patients. Data was analyzed by SPSS software. Mann-Whitney test, Roc Curve, and logistic regression model were used for data analysis. Results: The total number of admitted patients was 1007 of whom 90 (10.82%) died. One patient was excluded from the study. Out of 90 dead patients, 51 (56.6%) were male and 39 (43.3%) were female. There was no significant difference between the two groups regarding the gender (P > 0.05). The mean age of the study group (deceased) was 59 ± 21 and the mean age of the control group (alive) was 58 ± 21. The Mann-Whitney test showed that the result of H Formula was significantly different between the two groups, (126 ± 26 for the study group and 111 ± 22 for the control group). The cutoff for H Formula was equal to 112.5. Negative and positive predictive values, specificity and sensitivity were 0.85, 0.35, 0.57, and 0.70 respectively. Logistic regression results show that the H index contents independently affected the mortality of infected patients. Conclusion: With regard to the importance of measuring vital signs in diagnosis and determining the mortality in patients with infectious disease, the H (Heydari) formula can be valuable for evaluation and determination of mortality risk and consequently, early intervention. Patients with severe tachycardia, severe tachypnea and altered mental status that cannot be properly and quickly improved within 2 hours after admission via hydration and other measures are at higher risk of mortality.
基金supported by the National Key Research and Development Program of China(No.2017YFC0907003)the National Natural Science Foundation of China(No.81973116 and 81573229)the Joint Research Funds for Shandong University and Karolinska Institute(No.SDU-KI-2020-03)。
文摘Objective:China is one of the countries with the heaviest burden of gastric cancer(GC)in the world.Understanding the epidemiological trends and patterns of GC in China can contribute to formulating effective prevention strategies.Methods:The data on incidence,mortality,and disability-adjusted life-years(DALYs)of GC in China from1990 to 2019 were obtained from the Global Burden of Disease Study(2019).The estimated annual percentage change(EAPC)was calculated to evaluate the temporal trends of disease burden of GC,and the package Nordpred in the R program was used to perform an age-period-cohort analysis to predict the numbers and rates of incidence and mortality in the next 25 years.Results:The number of incident cases of GC increased from 317.34 thousand in 1990 to 612.82 thousand in2019,while the age-standardized incidence rate(ASIR)of GC decreased from 37.56 per 100,000 in 1990 to 30.64 per 100,000 in 2019,with an EAPC of-0.41[95%confidence interval(95%CI):-0.77,-0.06].Pronounced temporal trends in mortality and DALYs of GC were observed.In the next 25 years,the numbers of new GC cases and deaths are expected to increase to 738.79 thousand and 454.80 thousand,respectively,while the rates of incidence and deaths should steadily decrease.The deaths and DALYs attributable to smoking were different for males and females.Conclusions:In China,despite the fact that the rates of GC have decreased during the past three decades,the numbers of new GC cases and deaths increased,and will continue to increase in the next 25 years.Additional strategies are needed to reduce the burden of GC,such as screening and early detection,novel treatments,and the prevention of risk factors.
文摘The virus SARS-CoV2,which causes the Coronavirus disease COVID-19 has become a pandemic and has spread to every inhabited continent.Given the increasing caseload,there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness.We present a first step towards building an artificial intelligence(AI)framework,with predictive analytics(PA)capabilities applied to real patient data,to provide rapid clinical decision-making support.COVID-19 has presented a pressing need as a)clinicians are still developing clinical acumen given the disease’s novelty,and b)resource limitations in a rapidly expanding pandemic require difficult decisions relating to resource allocation.The objectives of this research are:(1)to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes,and(2)to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation.The predictive models learn from historical data to help predict specifically who will develop acute respiratory distress syndrome(ARDS),a severe outcome in COVID-19.Our experimental results based on two hospitals in Wenzhou,Zhejang,China identify features most predictive of ARDS in COVID-19 initial presentation which would not have stood out to clinicians.A mild increase in elevated alanine aminotransferase(ALT)(a liver enzyme)),a presence of myalgias(body aches),and an increase in hemoglobin,in this order,are the clinical features,on presentation,that are the most predictive.Those two centers’COVID-19 case series symptoms on initial presentation can help predict severe outcomes.Predictive models that learned from historical data of patients from two Chinese hospitals achieved 70%to 80%accuracy in predicting severe cases.
文摘Background Climate change presents an imminent threat to almost all biological systems across the globe.In recent years there have been a series of studies showing how changes in climate can impact infectious disease transmission.Many of these publications focus on simulations based on in silico data,shadowing empirical research based on feld and laboratory data.A synthesis work of empirical climate change and infectious disease research is still lacking.Methods We conducted a systemic review of research from 2015 to 2020 period on climate change and infectious diseases to identify major trends and current gaps of research.Literature was sourced from Web of Science and PubMed literary repositories using a key word search,and was reviewed using a delineated inclusion criteria by a team of reviewers.Results Our review revealed that both taxonomic and geographic biases are present in climate and infectious disease research,specifcally with regard to types of disease transmission and localities studied.Empirical investigations on vector-borne diseases associated with mosquitoes comprised the majority of research on the climate change and infectious disease literature.Furthermore,demographic trends in the institutions and individuals published revealed research bias towards research conducted across temperate,high-income countries.We also identifed key trends in funding sources for most resent literature and a discrepancy in the gender identities of publishing authors which may refect current systemic inequities in the scientifc feld.Conclusions Future research lines on climate change and infectious diseases should considered diseases of direct transmission(non-vector-borne)and more research efort in the tropics.Inclusion of local research in low-and middle-income countries was generally neglected.Research on climate change and infectious disease has failed to be socially inclusive,geographically balanced,and broad in terms of the disease systems studied,limiting our capacities to better understand the actual efects of climate change on health.
文摘Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.
文摘目的/意义系统梳理基于互联网数据的传染病预测模型相关研究,助力实现传染病监测关口前移,为构建传染病智慧化立体防治体系提供参考。方法/过程对Web of Science核心数据库和中国知网收录的近20年基于互联网数据的传染病监测预警研究发展历程及研究方向进行梳理,分析当前主要问题与挑战,总结常见预测模型及其优化方向。结果/结论互联网传染病监测研究呈监测疾病多样化、数据来源精细化和专业化等趋势。由于互联网数据的复杂性和不确定性,现有模型大多仅适用于短时或实时预测。通过构建组合模型、加强多源数据融合、完善关键词与影响因素选择等方式,可进一步优化模型,加强拟合效果和预测能力。
基金This work was supported in part by the New Generation Artificial Intelligence Development Plan of China(2015-2030)(Grant No.2021ZD0111205)the National Natural Science Foundation of China(Grant Nos.72025404,72293575 and 72074209).
文摘The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.
文摘以Web of Science数据库1945年以来的英文文献为依据,使用文献计量方法分析传染病史研究的文献数量和国家地区分布特征,描绘国际传染病史研究的知识图景,发现该领域研究热点集中在全球史视野的传染病史、公共卫生道德标准、新发和再发传染病、传染病起源、跨学科视角分析五个方面。从发展趋势看,新发和再发传染病的机制已被普遍接受,传染病中的道德伦理问题研究不足,目前“根除主义”仍存在争议,跨学科研究依然具有吸引力;从国际传染病史文献的发表趋势来看,传染病史研究正处于繁荣发展期;传染病史研究文献在其他领域被广泛引用。
