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.
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.
Study Type
OBSERVATIONAL
Enrollment
1,000,000
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.
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
RECRUITINGSecond Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
RECRUITINGArea 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).
Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 1 year
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