AI-Agent for Automated Diagnosis and Predicting Using EHR and Multimodal Data
Ориентир для пациента и семьи
Простыми словами
Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.
- Что изучают
- Это наблюдательное исследование: исследуемое лечение участникам по протоколу не назначают.
- Кому может быть актуально
- Состояния в реестре: AI Agent. Базовые параметры: Без ограничений · Все.
- Что важно проверить
- Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
- Где проводится
- Китай
- Следующий шаг
- Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
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Официальное название
AI-Agent Assisted Automation for Diagnosing and Predicting Patients Using Electronic Health Records and Multimodal Data
Обзор
The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.
Подробное описание
This multi-center, retrospective clinical study is designed to evaluate the application and effectiveness of an AI agent in the medical decision-making process. The AI agent integrates and analyzes multimodal data, including electronic health records (EHR) and various imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds) to predict and diagnose a range of diseases. By leveraging the power of machine learning and data fusion techniques, the AI agent can identify patterns in large and complex datasets, offering insights that may not be immediately apparent through traditional diagnostic methods.The study will compare the AI agent's diagnostic accuracy and disease prediction capabilities with traditional diagnostic practices to assess its potential benefits in clinical settings. Key questions include whether the AI agent can assist in early diagnosis, predict disease progression, and support healthcare professionals in making personalized treatment decisions. Participants will not be required to undergo any additional interventions; they will only provide historical health data, including EHR and relevant imaging data, which will be analyzed by the AI agent. The AI system will then use this data to assist healthcare providers by offering predictions and diagnostic suggestions based on the analysis of the multimodal information. The ultimate goal is to determine whether this AI-driven approach can improve diagnostic accuracy, optimize treatment strategies, and enhance patient outcomes in clinical practice.
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
- Participants must have comprehensive electronic health records (EHR) available, including demographic information, medical history, and laboratory results.
- Participants must have available multimodal imaging data (e.g., X-rays, CT scans, MRIs, ultrasounds) relevant to their health condition.
- Participants must have a confirmed diagnosis of one or more diseases or health conditions based on clinical records or imaging data.
- Patients must provide consent for the use of their historical health data for research purposes.
Критерии исключения
- Participants with ambiguous or unverifiable diagnoses that cannot be accurately categorized.
- Duplicate or redundant patient data (e.g., repeated records of the same patient without clear differentiation).
Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.
Здоровые добровольцы: Да
Дизайн исследования
- Модель наблюдения
- Только случаи
Центры проведения
Китай · 6 центров
- Nanfang Hospital — Гуанчжоу
- Sun Yat-Sen Memorial Hospital — Гуанчжоу
- Sun Yat-sen University Cancer Hospital — Гуанчжоу
- West China Hospital — Чэнду
- First Affiliated Hospital of Wenzhou Medical University — Wenzhou
- Second Affiliated Hospital of Wenzhou Medical University — Wenzhou
Идентификаторы
NCT: NCT06791499 · AI-agent