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1. 1. Risk Stratification of Acute Coronary Syndrome using Machine Learning : An analysis of CLEOS-CPDS data

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Glacier Ali; Rebecka Gustavsson; [2022]
   Nyckelord :Chest pain; Acute coronary syndrome; Computerized history taking; Machine learning; Risk stratification; Bröstsmärtor; Akuta koronara syndrom; Digitaliserad insamling av medicinsk bakgrund; Maskininlärning; Riskbedömning;

   Sammanfattning : Chest pain is one of the most common complaints amongst patients seeking urgent medical care at hospitals. Chest pain can be a symptom of serious cardiovascular disease such as acute coronary syndrome (ACS), however, most underlying causes are benign. Risk stratification in early stages of medical evaluation is difficult. LÄS MER