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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Angina manifests as chest pain, tightness, or squeezing discomfort typically located behind the breastbone. It can radiate to the neck, jaw, shoulders, and inner aspects of the upper arms, most commonly the left arm. Patients may experience shortness of breath, fatigue, profuse sweating, dizziness, indigestion, heartburn, palpitations, anxiety, and vomiting as accompanying symptoms. This pain often lasts a few minutes and is triggered by physical exertion, emotional stress, heavy meals, or cold...
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Cardiac emergencies are critical situations involving the heart that require immediate medical intervention to prevent severe complications or death. These emergencies often arise from underlying heart conditions that impair the heart's ability to function correctly.Types of Cardiac EmergenciesThe most common types of cardiac emergencies include Acute Coronary Syndrome (ACS), myocardial infarction (MI), cardiac arrest, and heart failure.Acute Coronary Syndrome (ACS)Acute Coronary Syndrome (ACS)...
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    This study predicts acute myocardial infarction (AMI) risk using patient history text and troponin levels. NLP models accurately identify high-risk patients for faster emergency care.

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    Area of Science:

    • Cardiology
    • Medical Informatics
    • Artificial Intelligence in Medicine

    Background:

    • Emergency departments face challenges in rapid acute myocardial infarction (AMI) risk stratification.
    • Anamnesis reports contain valuable patient data often underutilized.
    • Integrating natural language processing (NLP) can enhance diagnostic capabilities.

    Purpose of the Study:

    • To develop and evaluate an NLP model for predicting AMI risk using anamnesis reports.
    • To improve the accuracy of risk stratification in emergency settings.
    • To assess the added value of combining textual data with troponin measurements.

    Main Methods:

    • Fine-tuning a pre-trained NLP model on patient anamnesis reports.
    • Utilizing the European Society of Cardiology (ESC) 0-1h algorithm for troponin evaluation.
    • Analyzing a dataset of chest pain patients from a Spanish emergency department.

    Main Results:

    • The NLP model achieved an accuracy of 0.975, sensitivity of 1.0, and specificity of 0.95 when combining anamnesis text with the first troponin measurement.
    • Models using only textual data showed limited clinical utility.
    • The integration of troponin measurements significantly improved model performance.

    Conclusions:

    • NLP shows significant potential as a diagnostic support tool in emergency departments.
    • The proposed model enables precise identification of high-risk AMI patients for timely intervention.
    • Combining NLP with troponin measurements enhances model reliability for clinical practice.