Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to fi nd a light-weight,convenient prediction method through machine learning.METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation.RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the fi ve most important features were acuity,arrival transportation,age,shock index,and respiratory rate.CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

基于数据挖掘技术的税务风险检测方法评述

Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive attention.To promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide.More specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk detection.It then focuses on data-mining-based tax risk detection methods utilized around the world.Based on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and analyzed.Finally,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled information.After investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.

Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

Data-driven analysis of chemicals,proteins and pathways associated with peanut allergy:from molecular networking to biological interpretation

Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fish and shellfish are responsible for more than 90%of food allergies.Here,we provide promising insights using a large-scale data-driven analysis,comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts,tree nuts(walnuts,almonds,cashews,pecans and pistachios)and soybean.Additionally,we have analysed the chemical compositions of peanuts in different processed form raw,boiled and dry-roasted.Using the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors(PPARs)and its isoform and their interaction with dietary lipids may have significant effect on allergic response.The results obtained from this study will direct future experimeantal and clinical studies to understand the role of dietary lipids and PPARisoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity.

CSEP-CN:Parameter Optimization of Pattern Informatics Method

Since the establishment of the Collaboratory for the Study of Earthquake Predictability,China(CSEP-CN)center,no comprehensive study has been conducted on the parameter models of the Pattern Informatics(PI)method within the China Seismic Experimental Site(CSES)region.Additionally,the boundary issues of the study area have been a subject of ongoing debate.Tian et al.(2024)indicates that variations in seismic activity within the region impact the predictive efficacy of the PI method.

The Collaboratory for the Study of Earthquake Predictability in China:Experiment Design and Preliminary Results of CSEP2.0

Since the inaugural international collaboration under the framework of the Collaboratory for the Study of Earthquake Predictability(CSEP)in 2007,numerous forecast models have been developed and operated for earthquake forecasting experiments across CSEP testing centers(Schorlemmer et al.,2018).Over more than a decade,efforts to compare forecasts with observed earthquakes using numerous statistical test methods and insights into earthquake predictability,which have become a highlight of the CSEP platform.

The forecasting efficiency under different selected regions by Pattern Informatics Method and seismic potential estimation in the North-South Seismic Zone

In 2022,four earthquakes with M_(S)≥6.0 including the Menyuan M_(S)6.9 and Luding M_(S)6.8 earthquakes occurred in the North-South Seismic Zone(NSSZ),which demonstrated high and strong seismicity.Pattern Informatics(PI)method,as an effective long and medium term earthquake forecasting method,has been applied to the strong earthquake forecasting in Chinese mainland and results have shown the positive performance.The earthquake catalog with magnitude above M_(S)3.0 since 1970 provided by China Earthquake Networks Center was employed in this study and the Receiver Operating Characteristic(ROC)method was applied to test the forecasting efficiency of the PI method in each selected region related to the North-South Seismic Zone systematically.Based on this,we selected the area with the best ROC testing result and analyzed the evolution process of the PI hotspot map reflecting the small seismic activity pattern prior to the Menyuan M_(S)6.9 and Luding M_(S)6.8 earthquakes.A“forward”forecast for the area was carried out to assess seismic risk.The study shows the following.1)PI forecasting has higher forecasting efficiency in the selected study region where the difference of seismicity in any place of the region is smaller.2)In areas with smaller differences of seismicity,the activity pattern of small earthquakes prior to the Menyuan M_(S)6.9 and Luding M_(S)6.8 earthquakes can be obtained by analyzing the spatio-temporal evolution process of the PI hotspot map.3)The hotspot evolution in and around the southern Tazang fault in the study area is similar to that prior to the strong earthquakes,which suggests the possible seismic hazard in the future.This study could provide some ideas to the seismic hazard assessment in other regions with high seismicity,such as Japan,Californi,Turkey,and Indonesia.

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Optimization and Construction of Collaborative Office under the Background of Smart Campus in Universities

At present, with the continuous development of technologies such as the Internet, big data, and artificial intelligence, smart campuses in universities are being rapidly constructed. Improving the informatization level of administrative management work is also an important content. The collaborative office work in multiple departments requires more standardized, convenient, intelligent, and secure office systems. In response to this issue, this article analyzes the optimization and construction process of collaborative office systems based on the development of university informatization, summarizes the operational results, and explores the prospects of smart office.

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。

Informatics and Health征稿启事

Informatics and Health(《信息学与健康》)是由中国医学科学院北京协和医学院主办,中国医学科学院医学信息研究所与科爱公司合作编辑出版,旨在反映医学卫生健康领域与信息科学技术相关的前沿学术研究进展的英文期刊(ISSN:2949-9534),本刊由中国工程院院士、中国医学科学院北京协和医学院院校长王辰教授担任主编,中国医学科学院医学信息研究所所长刘辉研究员担任执行主编。