AI-Driven Prediction of Hospital-Acquired Infections With EHR

Официальное название

Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест 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

Первичные конечные точки

• Area Under the Curve (AUC) [Срок оценки: 1 year]
• F1 Score [Срок оценки: 1 year]

Вторичные конечные точки (2)

• Sensitivity (True Positive Rate) [Срок оценки: 1 year]
• Specificity (True Negative Rate) [Срок оценки: 1 year]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Случай-контроль

Центры проведения

Китай · 2 центра

• First Affiliated Hospital of Wenzhou Medical University — Wenzhou
• Second Affiliated Hospital of Wenzhou Medical University — Wenzhou

Идентификаторы

NCT: NCT06791382 · Hospital-Acquired Infections

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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