Health TrialsbyHealthPanel

AI-Driven Prediction of Hospital-Acquired Infections With EHR

ConditionHospital-acquired Infections
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age0-90
SponsorThe Eye Hospital of Wenzhou Medical University

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Patients with complete and accessible EHR data, including medical history, laboratory test results, treatment regimens, clinical observations, and infection history.

Patients who have been admitted to the participating hospital or healthcare facility during the study period.

All participants must provide informed consent to use their health data for research purposes.

Disqualifiers

Patients with incomplete or missing critical EHR data, such as lab results, medical history, or treatment details, which are necessary for infection prediction.

Patients who have severe cognitive disorders, dementia, or conditions that prevent them from providing informed consent or participating in the study.

Patients who have not been admitted to the hospital during the study period or who are receiving outpatient care only.

Patients with terminal conditions where infection prediction may not be applicable to the clinical goals of the study.

Trial population

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.

Trial design

Design model

Case-control

Time perspective

Other

Treatments tested in this trial

  • AI-Based Diagnostic and Prognostic Model

    Diagnostic test

    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.

Treatment groups

1,000,000 Participants
are divided into 2 treatment groups
Group A: Hospital-Acquired Infection Cohort1 intervention
Group B: Healthy Cohort (No HAI)1 intervention

Trial outcomes

Primary outcomes

1

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).

Time frame
1 year
2

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

Secondary outcomes

1

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
2

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

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

The Eye Hospital of Wenzhou Medical University

Lead sponsor

Wenzhou Medical University

Sponsor institution

Source ClinicalTrials.gov
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