Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate becau...Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.展开更多
Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical I...Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.展开更多
Widely used in clinical research, the database is a new type of data management automation technology and the most efficient tool for data management. In this article, we first explain some basic concepts, such as the...Widely used in clinical research, the database is a new type of data management automation technology and the most efficient tool for data management. In this article, we first explain some basic concepts, such as the definition, classification, and establishment of databases. Afterward, the workflow for establishing databases, inputting data, verifying data, and managing databases is presented. Meanwhile, by discussing the application of databases in clinical research, we illuminate the important role of databases in clinical research practice. Lastly, we introduce the reanalysis of randomized controlled trials(RCTs) and cloud computing techniques, showing the most recent advancements of databases in clinical research.展开更多
<strong>Objective: </strong>To explore those differences and relationships of the initial diagnostic clinical data between confirmed cases of 2019-nCoV and suspected cases of COVID-19, and then to establis...<strong>Objective: </strong>To explore those differences and relationships of the initial diagnostic clinical data between confirmed cases of 2019-nCoV and suspected cases of COVID-19, and then to establish prediction models for predicting the probability of the first diagnosis of 2019-nCoV. <strong>Methods:</strong> A total of 81 suspected cases and 87 confirmed cases of moderate 2019-nCoV diagnosed initially in the isolation wards of the First People’s Hospital of Wuhu and the People’s Hospital of Wuwei and Wuhan Caidian Module Hospital with the help of our hospital doctors were gathered, and retrospectively analyzed. <strong>Results:</strong> The most common symptoms were fever (76.79%) and cough (64.29%) in the total of 168 cases. The median age was 45 (35 - 56) years old in the 87 confirmed cases of moderate 2019-nCoV, older than the median age 36 (29 - 50) in the 81 suspected cases. There were significant more in the former than in the latter in the incidence of myalgia, ground-glass opacity (GGO), invasions of lesion in the peripheral lobes, vascular thickening and bronchial wall thickening, interlobular septal thicking, and small pulmonary nodules. On the contrary, there were less in the former than in the latter in the total number of leukocytes and neutrophils in blood routine examination and the levels of procalcitonin (PCT). Two groups were statistically significantly different (<em>P</em> < 0.05). Multivariate logistic regression analysis showed that age, fever, myalgia, GGO, vascular thickening and bronchial wall thickening, invasions of lesion in the peripheral lobes were independent factors for identification of 2019-nCoV, and the total number of leukocytes, cough, pharyngalgia and headache were negatively related. The established mathematical equation for predicting model for predicting the probability of the first diagnosis of 2019-nCoV is: <em>P</em> = e<sup><em>x</em></sup>/(1 + e<sup><em>x</em></sup>), <em>x</em> = <span style="white-space:nowrap;">−</span>6.226 + (0.071 × ages) + (1.720 × fever) + (2.858 × myalgia) + (2.131 × GGO) + (3.000 × vascular thickening and bron-chial wall thickening) + (3.438 × invasions of lesion in the peripheral lobes) + (<span style="white-space:nowrap;">−</span>0.304 × the number of leukocytes) + (<span style="white-space:nowrap;">−</span>1.478 × cough) + (<span style="white-space:nowrap;">−</span>1.830 × pharyngalgia) + (<span style="white-space:nowrap;">−</span>2.413 × headache), where e is a natural logarithm. The area under the ROC curve (AUC) of this model was calculated to be 0.945 (0.915 - 0.976). The sensitivity is 0.920 and the specificity is 0.827 when the appropriate critical point is 0.360.<strong> Conclusions: </strong>A mathematical equation prediction model for predicting the probability of the first diagnosis of 2019-nCoV can be established based on the initial diagnostic clinical data of moderate 2019-nCoV. The prediction model is a good assistant diagnostic method for its high accurateness.展开更多
Background:Sharing biological material and clinical data from patients with uveal melanoma.Methods:Uveal melanoma is the most common intraocular malignancy in the adult population.Because uveal melanoma is primarily a...Background:Sharing biological material and clinical data from patients with uveal melanoma.Methods:Uveal melanoma is the most common intraocular malignancy in the adult population.Because uveal melanoma is primarily a sporadic cancer and familial cases are rare,it is difficult to prevent or detect it.Despite effective treatment of ocular tumors,more than 50%of patients develop incurable liver metastases mainly in the 5-10 years following the detection of the primary tumor.This cancer is relatively rare and the obtained biopsies are very small.About 20 samples are taken each year in Quebec.This provincial infrastructure is made of biological material from donors with uveal melanoma and a large clinical database.Collected tumor biopsies are used for culturing cell lines and the creation of a DNA/RNA library used for genomic and genetic studies.Results:This infrastructure plays an important role in the achievement of various research programs for a better understanding of genetic and environmental factors involved in the development of melanoma and the spread of metastasis.It allows collaboration with other researchers at a provincial,national and international level in order to make progress in basic and clinical research on uveal melanoma.Conclusions:The biological material and clinical data of this infrastructure are available upon request to VHRN members whose research project was approved by the ethics committee of the institution.展开更多
Graphical abstracts(GAs)are emerging as a pivotal tool in medical literature,enhancing the dissemination and comprehension of complex clinical data through visual summaries.This editorial highlights the significant ad...Graphical abstracts(GAs)are emerging as a pivotal tool in medical literature,enhancing the dissemination and comprehension of complex clinical data through visual summaries.This editorial highlights the significant advantages of GAs,including improved clarity,increased reader engagement,and enhanced visibility of research findings.By transforming intricate scientific data into accessible visual formats,these abstracts facilitate quick and effective knowledge transfer,crucial in clinical decision-making and patient care.However,challenges such as potential data misrepresentation due to oversimplification,the skill gap in graphic design among researchers,and the lack of standardized creation guidelines pose barriers to their widespread adoption.Additionally,while software such as Adobe Illustrator,BioRender,and Canva are commonly employed to create these visuals,not all researchers may be proficient in their use.To address these issues,we recommend that academic journals establish clear guidelines and provide necessary design training to researchers.This proactive approach will ensure the creation of high-quality GAs,promote their standardization,and expand their use in clinical reporting,ultimately benefiting the medical community and improving healthcare outcomes.展开更多
Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face ...Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.展开更多
Postoperative infection is a major global health concern,affecting 5%-10%of surgical patients and nearly doubling mortality in severe cases[1].Transplant recipients are particularly vulnerable,with 30%-80%developing i...Postoperative infection is a major global health concern,affecting 5%-10%of surgical patients and nearly doubling mortality in severe cases[1].Transplant recipients are particularly vulnerable,with 30%-80%developing infections within 30 days,often from opportunistic pathogens[2,3].Key risk factors include epidemiological exposure,net immunosuppression,age,transplant type,and surgical history[4].Despite known infection risks,current evidence remains transplantation type-specific and neglects behavioral modulators[5].Different types of transplantation may share similar risk factors[6].To identify common factors affecting postoperative infection,this study collected standardized clinical data-including diet,psychological response,medication use,and biochemical indicators-from liver and kidney transplant patients across six hospitals using a unified standard operating procedure(SOP).展开更多
文摘Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.
基金the National Social Science Foundation of China(No.16BGL183).
文摘Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.
基金supported by Fundamental Research Funds of State Key Laboratory of Ophthalmology (Grant No.2015QN01)Young Teacher Top-Support project of Sun Yat-sen University(Grant No.2015ykzd11)+4 种基金the Cultivation Projects for Young Teaching Staff of Sun Yat-sen University(Grant No.12ykpy61) from the Fundamental Research Funds for the Central Universitiesthe Pearl River Science and Technology New Star(Grant No.2014J2200060)Project of Guangzhou City,the Guangdong Provincial Natural Science Foundation for Distinguished Young Scholars of China(Grant No. 2014A030306030)Youth Science and Technology Innovation Talents Funds in Special Support Plan for High Level Talents in Guangdong Province(Grant No. 2014TQ01R573)Key Research Plan for National Natural Science Foundation of China in Cultivation Project (No.91546101)
文摘Widely used in clinical research, the database is a new type of data management automation technology and the most efficient tool for data management. In this article, we first explain some basic concepts, such as the definition, classification, and establishment of databases. Afterward, the workflow for establishing databases, inputting data, verifying data, and managing databases is presented. Meanwhile, by discussing the application of databases in clinical research, we illuminate the important role of databases in clinical research practice. Lastly, we introduce the reanalysis of randomized controlled trials(RCTs) and cloud computing techniques, showing the most recent advancements of databases in clinical research.
文摘<strong>Objective: </strong>To explore those differences and relationships of the initial diagnostic clinical data between confirmed cases of 2019-nCoV and suspected cases of COVID-19, and then to establish prediction models for predicting the probability of the first diagnosis of 2019-nCoV. <strong>Methods:</strong> A total of 81 suspected cases and 87 confirmed cases of moderate 2019-nCoV diagnosed initially in the isolation wards of the First People’s Hospital of Wuhu and the People’s Hospital of Wuwei and Wuhan Caidian Module Hospital with the help of our hospital doctors were gathered, and retrospectively analyzed. <strong>Results:</strong> The most common symptoms were fever (76.79%) and cough (64.29%) in the total of 168 cases. The median age was 45 (35 - 56) years old in the 87 confirmed cases of moderate 2019-nCoV, older than the median age 36 (29 - 50) in the 81 suspected cases. There were significant more in the former than in the latter in the incidence of myalgia, ground-glass opacity (GGO), invasions of lesion in the peripheral lobes, vascular thickening and bronchial wall thickening, interlobular septal thicking, and small pulmonary nodules. On the contrary, there were less in the former than in the latter in the total number of leukocytes and neutrophils in blood routine examination and the levels of procalcitonin (PCT). Two groups were statistically significantly different (<em>P</em> < 0.05). Multivariate logistic regression analysis showed that age, fever, myalgia, GGO, vascular thickening and bronchial wall thickening, invasions of lesion in the peripheral lobes were independent factors for identification of 2019-nCoV, and the total number of leukocytes, cough, pharyngalgia and headache were negatively related. The established mathematical equation for predicting model for predicting the probability of the first diagnosis of 2019-nCoV is: <em>P</em> = e<sup><em>x</em></sup>/(1 + e<sup><em>x</em></sup>), <em>x</em> = <span style="white-space:nowrap;">−</span>6.226 + (0.071 × ages) + (1.720 × fever) + (2.858 × myalgia) + (2.131 × GGO) + (3.000 × vascular thickening and bron-chial wall thickening) + (3.438 × invasions of lesion in the peripheral lobes) + (<span style="white-space:nowrap;">−</span>0.304 × the number of leukocytes) + (<span style="white-space:nowrap;">−</span>1.478 × cough) + (<span style="white-space:nowrap;">−</span>1.830 × pharyngalgia) + (<span style="white-space:nowrap;">−</span>2.413 × headache), where e is a natural logarithm. The area under the ROC curve (AUC) of this model was calculated to be 0.945 (0.915 - 0.976). The sensitivity is 0.920 and the specificity is 0.827 when the appropriate critical point is 0.360.<strong> Conclusions: </strong>A mathematical equation prediction model for predicting the probability of the first diagnosis of 2019-nCoV can be established based on the initial diagnostic clinical data of moderate 2019-nCoV. The prediction model is a good assistant diagnostic method for its high accurateness.
文摘Background:Sharing biological material and clinical data from patients with uveal melanoma.Methods:Uveal melanoma is the most common intraocular malignancy in the adult population.Because uveal melanoma is primarily a sporadic cancer and familial cases are rare,it is difficult to prevent or detect it.Despite effective treatment of ocular tumors,more than 50%of patients develop incurable liver metastases mainly in the 5-10 years following the detection of the primary tumor.This cancer is relatively rare and the obtained biopsies are very small.About 20 samples are taken each year in Quebec.This provincial infrastructure is made of biological material from donors with uveal melanoma and a large clinical database.Collected tumor biopsies are used for culturing cell lines and the creation of a DNA/RNA library used for genomic and genetic studies.Results:This infrastructure plays an important role in the achievement of various research programs for a better understanding of genetic and environmental factors involved in the development of melanoma and the spread of metastasis.It allows collaboration with other researchers at a provincial,national and international level in order to make progress in basic and clinical research on uveal melanoma.Conclusions:The biological material and clinical data of this infrastructure are available upon request to VHRN members whose research project was approved by the ethics committee of the institution.
文摘Graphical abstracts(GAs)are emerging as a pivotal tool in medical literature,enhancing the dissemination and comprehension of complex clinical data through visual summaries.This editorial highlights the significant advantages of GAs,including improved clarity,increased reader engagement,and enhanced visibility of research findings.By transforming intricate scientific data into accessible visual formats,these abstracts facilitate quick and effective knowledge transfer,crucial in clinical decision-making and patient care.However,challenges such as potential data misrepresentation due to oversimplification,the skill gap in graphic design among researchers,and the lack of standardized creation guidelines pose barriers to their widespread adoption.Additionally,while software such as Adobe Illustrator,BioRender,and Canva are commonly employed to create these visuals,not all researchers may be proficient in their use.To address these issues,we recommend that academic journals establish clear guidelines and provide necessary design training to researchers.This proactive approach will ensure the creation of high-quality GAs,promote their standardization,and expand their use in clinical reporting,ultimately benefiting the medical community and improving healthcare outcomes.
基金Supported by the National Major Scientific and Technological Special Project for"Significant New Drugs Development’’(No.2018ZX09201008)Special Fund Project for Information Development from Shanghai Municipal Commission of Economy and Information(No.201701013)
文摘Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.
基金the MOST Key Research and Development Program of China(grant number 2022YFC2304703)the Natural Science Foundation of China(grant number 32422004)+5 种基金The Medicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University(grant number 24X010301328)the Natural Science Foundation of China(grant number 32270202)the Computational Biology Program of Science and Technology Commission of Shanghai Municipality(STCSM)(grant number 25JS2810200)the MOST Key Research and Development Program of China(grant number 2020YFA0907200)Program of Shanghai Academic Research Leader(grant number 23XD1422300)Innovative research team of high-level local universities in Shanghai.All funding sources are attributed to N.N.L.
文摘Postoperative infection is a major global health concern,affecting 5%-10%of surgical patients and nearly doubling mortality in severe cases[1].Transplant recipients are particularly vulnerable,with 30%-80%developing infections within 30 days,often from opportunistic pathogens[2,3].Key risk factors include epidemiological exposure,net immunosuppression,age,transplant type,and surgical history[4].Despite known infection risks,current evidence remains transplantation type-specific and neglects behavioral modulators[5].Different types of transplantation may share similar risk factors[6].To identify common factors affecting postoperative infection,this study collected standardized clinical data-including diet,psychological response,medication use,and biochemical indicators-from liver and kidney transplant patients across six hospitals using a unified standard operating procedure(SOP).
