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At the emergency department, it is important to quickly and accurately identify patients at risk of acute myocardial infarction (AMI). One of the main tools for detecting AMI is the electrocardiogram (ECG), which can be difficult to interpret manually. There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-qual
ObjectivesThe objective was to develop an artificial intelligence (AI) model for predicting acute coronary occlusion myocardial infarction (OMI) in patients with chest pain at the emergency department (ED), using information that is widely available early in the ED assessment.MethodsIn a cohort of 24,511 consecutive adult ED patients with chest pain from 5 Swedish hospitals, OMI cases were identif
Hemslöjdspionjären Lilli Zickerman (1858–1949), som tog initiativ till bildandet av Föreningen för svensk hemslöjd, var under nära fyra decennier bosatt i Vittsjö. Det var därifrån som hon tillsammans med flera familjemedlemmar, som också flyttat dit, arbetade med en stor inventering av svensk folklig textilkonst. Inventeringsmaterialet, som bär namnet ”Föreningen för svensk hemslöjds samlingsverk
BACKGROUND: In the European Union alone, more than 100 million people present to the emergency department (ED) each year, and this has increased steadily year-on-year by 2-3%. Better patient management decisions have the potential to reduce ED crowding, the number of diagnostic tests, the use of inpatient beds, and healthcare costs.METHODS: We have established the Skåne Emergency Medicine (SEM) co
