[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100576906":3},{"organization":4,"outcomesModule":7,"designInfo":24,"detailedDescription":26,"studyPopulation":27,"armGroups":28,"interventions":38,"overallOfficials":23,"centralContacts":44,"locations":50,"responsibleParty":78,"collaborators":23,"id":83,"slug":23,"hasResults":84,"nctId":85,"briefTitle":86,"officialTitle":87,"acronym":23,"eligibilityCriteria":88,"healthyVolunteers":89,"sex":90,"minAge":91,"maxAge":92,"enrollmentInfo":93,"targetDuration":23,"studyType":96,"phases":23,"briefSummary":97,"conditions":98,"keywords":23,"overallStatus":53,"whyStopped":23,"lastUpdateSubmitDate":100,"lastUpdatePostDateStruct":101,"startDateStruct":104,"completionDateStruct":106,"leadSponsor":108,"locationsCount":109},{"fullName":5,"class":6},"The Eye Hospital of Wenzhou Medical University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":16,"otherOutcomes":23},[9,13],{"measure":10,"description":11,"timeFrame":12},"Area Under the Curve (AUC)","AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).","1 year",{"measure":14,"description":15,"timeFrame":12},"F1 Score","The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.",[17,20],{"measure":18,"description":19,"timeFrame":12},"Sensitivity (True Positive Rate)","Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.",{"measure":21,"description":22,"timeFrame":12},"Specificity (True Negative Rate)","Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative.",null,{"allocation":23,"interventionModel":23,"interventionModelDescription":23,"primaryPurpose":23,"observationalModel":25,"timePerspective":6,"maskingInfo":23},"CASE_CONTROL","Hospital-acquired infections (HAIs) are a significant cause of morbidity and mortality in healthcare settings. Early identification and prevention of HAIs are crucial for improving patient outcomes, reducing healthcare costs, and preventing the spread of infections. In clinical practice, healthcare providers often need to integrate a wide range of patient data, including medical history, laboratory test results, medication usage, surgical procedures, and clinical observations, to assess infection risks and prevent HAIs. As infection control and precision medicine become increasingly important, the challenge remains to predict and prevent infections, especially in patients with subtle or asymptomatic risk factors. Recent advancements in artificial intelligence and data analysis techniques have shown great promise in improving the accuracy and efficiency of infection prediction and prevention. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, lab results, clinical observations, and patient demographics. The objective is to enhance the early identification of patients at risk for HAIs, streamline clinical workflows, and optimize infection control measures. Ultimately, this system seeks to reduce the incidence of hospital-acquired infections, improve patient safety, and enhance overall healthcare quality.","The study population consists of individuals who have received care at participating study centers. Participants must have comprehensive electronic health records (EHRs) available, including medical history, laboratory test results, treatment data, and clinical observations. Both individuals who have developed hospital-acquired infections (HAIs) and those who have not will be included in the study to evaluate the AI-assisted model's predictive capabilities for infection risk. The study will focus on patients with complete and documented care records from the participating centers, ensuring a diverse cohort for analysis across different age groups and infection types.",[29,34],{"label":30,"type":23,"description":31,"interventionNames":32},"Hospital-Acquired Infection Cohort","This group consists of patients who have developed a hospital-acquired infection (HAI) during their hospital stay. Participants in this cohort will be used to evaluate the effectiveness of the AI-assisted predictive model in identifying the risk factors leading to hospital-acquired infections. The model will be assessed based on the accuracy of predicting infection risks in hospitalized patients. No specific interventions will be provided as part of this cohort beyond the existing hospital infection control practices.",[33],"Diagnostic Test: AI-Based Diagnostic and Prognostic Model",{"label":35,"type":23,"description":36,"interventionNames":37},"Healthy Cohort (No HAI)","This group consists of patients who have not developed any hospital-acquired infections during their hospital stay. Participants in this cohort will serve as the control group for comparison against the experimental group. The AI-assisted model will be evaluated for its ability to distinguish between patients who are at risk for developing infections and those who remain infection-free during hospitalization. No interventions will be provided as part of this cohort, as they represent patients without infection-related complications.",[33],[39],{"type":40,"name":41,"description":42,"armGroupLabels":43,"otherNames":23},"DIAGNOSTIC_TEST","AI-Based Diagnostic and Prognostic Model","This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, clinical observations, and treatment data, to predict the risk of hospital-acquired infections (HAIs). The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of patients at risk for infections. By analyzing historical health data, the model aims to predict potential infection developments, improving early detection, prevention strategies, and patient outcomes in hospital settings.",[35,30],[45],{"name":46,"role":47,"phone":48,"phoneExt":23,"email":49},"Fei Liu, MD","CONTACT","+86 13810512704","liufei_2359@163.com",[51,68],{"facility":52,"status":53,"city":54,"state":55,"zip":23,"country":56,"countryCode":57,"cosmosGeoPoint":58,"geoPoint":63,"contacts":64},"First Affiliated Hospital of Wenzhou Medical University","RECRUITING","Wenzhou","Zhejiang","China","CN",{"type":59,"coordinates":60},"Point",[61,62],120.66682,27.99942,{"lat":62,"lon":61},[65],{"name":66,"role":47,"phone":23,"phoneExt":23,"email":67},"Cheng Tang","c249325687@163.com",{"facility":69,"status":53,"city":54,"state":55,"zip":23,"country":56,"countryCode":57,"cosmosGeoPoint":70,"geoPoint":72,"contacts":73},"Second Affiliated Hospital of Wenzhou Medical University",{"type":59,"coordinates":71},[61,62],{"lat":62,"lon":61},[74],{"name":75,"role":47,"phone":76,"phoneExt":23,"email":77},"Sian Liu","+86-0577-88002888","liusan@mail3.sysu.edu.cn",{"type":79,"investigatorFullName":80,"investigatorTitle":81,"investigatorAffiliation":82,"oldNameTitle":23,"oldOrganization":23},"PRINCIPAL_INVESTIGATOR","Kang Zhang","Chief Scientist","Wenzhou Medical University","100576906",false,"NCT06791382","AI-Driven Prediction of Hospital-Acquired Infections With EHR","Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach","Inclusion Criteria:\n\n1. Patients with complete and accessible EHR data, including medical history, laboratory test results, treatment regimens, clinical observations, and infection history.\n2. Patients who have been admitted to the participating hospital or healthcare facility during the study period.\n3. All participants must provide informed consent to use their health data for research purposes.\n\nExclusion Criteria:\n\n1. Patients with incomplete or missing critical EHR data, such as lab results, medical history, or treatment details, which are necessary for infection prediction.\n2. Patients who have severe cognitive disorders, dementia, or conditions that prevent them from providing informed consent or participating in the study.\n3. Patients who have not been admitted to the hospital during the study period or who are receiving outpatient care only.\n4. Patients with terminal conditions where infection prediction may not be applicable to the clinical goals of the study.",true,"ALL","0 Years","90 Years",{"count":94,"type":95},1000000,"ESTIMATED","OBSERVATIONAL","This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing infection, leveraging multimodal health data.",[99],"Hospital-acquired Infections","2025-04-16",{"date":102,"type":103},"2025-04-17","ACTUAL",{"date":105,"type":103},"2023-02-01",{"date":107,"type":95},"2025-05",{"name":5,"class":6},2]