“Artificial Intelligence (AI) Based Clinical Decision Support System (CDSS) for Acute Emergency Care (AEC) of Stemi Patients Based on Standardized Management Protocol at Parul Sevasthram Hospital, Vadodara, Gujarat.”

Background
Acute emergency care (AEC) for ST-segment elevation myocardial infarction (STEMI) is a critical area in cardiology, where timely and accurate decisions can significantly impact patient outcomes. STEMI, a severe form of heart attack, occurs due to the complete blockage of a coronary artery, leading to substantial myocardial damage if not treated promptly. Traditional management protocols for STEMI, such as the guidelines provided by the American College of Cardiology (ACC) and the American Heart Association (AHA), emphasize rapid diagnosis, timely reperfusion therapy, and continuous monitoring. However, the complexity and urgency of these cases present challenges that can benefit from advanced technological interventions, particularly AI-based Clinical Decision Support Systems (CDSS).
Current Challenges in STEMI Management
The management of STEMI involves several critical steps, including early recognition, risk stratification, selection of appropriate therapeutic interventions, and post-treatment monitoring. These steps require the integration of vast amounts of clinical data, rapid decision-making, and coordination among multidisciplinary teams. Despite established protocols, variability in clinical practice and delays in treatment initiation often occur, leading to suboptimal patient outcomes. Factors contributing to these challenges include: Data Overload, Time Sensitivity, and Clinical Variability
Development and Integration of AI-Based CDSS
The development of an AI-based CDSS for STEMI involves several stages, including data collection, algorithm training, system validation, and integration into clinical practice. This process requires collaboration between cardiologists, data scientists, and IT specialists. Key steps include:
1. Data Collection and Preprocessing: Aggregating and standardizing data from various sources, such as EHRs, imaging systems, and wearable devices, ensuring data quality and consistency.
2. Algorithm Development, Training machine learning models on large datasets to recognize patterns and make predictions. This involves selecting appropriate features, tuning model parameters, and evaluating performance using metrics like accuracy, sensitivity, and specificity.
3. Clinical Validation: Testing the AI system in real-world settings to assess its reliability, safety, and effectiveness. This involves pilot studies, randomized controlled trials, and feedback from clinicians.

Cardiac Emergency

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