AI-Driven Prediction of Hospital-Acquired Infections With EHR
Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach
The Eye Hospital of Wenzhou Medical University
1,000,000 participants
Feb 1, 2023
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria3
- 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.
Exclusion Criteria4
- 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.
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Interventions
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.
Locations(2)
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NCT06791382