Hospital-acquired Infections
Conditions
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAI-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.
Sponsors
The Eye Hospital of Wenzhou Medical University
Study design
Observational model
CASE_CONTROL
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
0 Years to 90 Years
Healthy volunteers
Yes
Inclusion criteria
1. Patients with complete and accessible EHR data, including medical history, laboratory test results, treatment regimens, clinical observations, and infection history. 2. Patients who have been admitted to the participating hospital or healthcare facility during the study period. 3. All participants must provide informed consent to use their health data for research purposes.
Exclusion criteria
1. Patients with incomplete or missing critical EHR data, such as lab results, medical history, or treatment details, which are necessary for infection prediction. 2. Patients who have severe cognitive disorders, dementia, or conditions that prevent them from providing informed consent or participating in the study. 3. Patients who have not been admitted to the hospital during the study period or who are receiving outpatient care only. 4. Patients with terminal conditions where infection prediction may not be applicable to the clinical goals of the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Curve (AUC) | 1 year | 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). |
| F1 Score | 1 year | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity (True Positive Rate) | 1 year | Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders. |
| Specificity (True Negative Rate) | 1 year | 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. |
Countries
China
Contacts
Primary ContactFei Liu, MD
Outcome results
None listed