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Profilbild för Finding subtle signals in ECGs to predict causes of death, applying AI on ECG tracing.

Finding subtle signals in ECGs to predict causes of death, applying AI on ECG tracing.

Föreläsare: Markus Lingman, Thomas Wallenfeldt, Ziad Obermayer

Spår: Datadriven vård och omsorgsförbättring

Screening for myocardial infarction, stroke, sudden death can be challenging due to the low diagnostic yield of a single electrocardiograph (ECG) to detect an often fleeting arrhythmia and the cumbersome nature of prolonged monitoring. Clinical risk scores can be used to identify

patients at risk but have only modest performance. Due to these limitations, major medical societies have issued inconsistent guidelines on atrial fibrillation screening. A low-cost, widely available, and non-invasive test that facilitates identification of patients who are likely to have atrial fibrillation would have important diagnostic and therapeutic implications. For instance, up to a third of strokes have no known cause—so-called embolic stroke  of undetermined source . 

Region Halland wast research database includes all ECG’s over a 8 year period. By combining the ECG with cause of death we trained a model to predict these outcomes.










Management/decision makers
Organizational development
Healthcare professionals




Profilbild för Markus Lingman

Markus Lingman Föreläsare

Strateg, sjukhusledningen på Hallands Sjukhus
Region Halland

Markus senior consultant and head of strategy at Region Halland. Winner of the Swedish AI person of the year award 2020.

Profilbild för Thomas Wallenfeldt

Thomas Wallenfeldt Föreläsare

Datadriven hälso och sjukvård

Thomas är ansvarig för området Datadriven Hälso- sjukvård på CGI. Han har som specialområde hälsa/sjukvård, dataanalys och datadriven beslutsfattande, strategisk planering inom hälso-sjukvård.

Ziad Obermayer Föreläsare

Ziad Obermeyer is an Acting Associate Professor of Health Policy and Management at the UC Berkeley School of Public Health, where he does research at the intersection of machine learning, medicine, and health policy. He previously was an Assistant Professor at Harvard Medical School, where he received the Early Independence Award, the National Institutes of Health’s most prestigious award for exceptional junior scientists. He continues to practice emergency medicine in underserved parts of the US. Prior to his career in medicine, he worked as a consultant to pharmaceutical and global health clients at McKinsey & Co. in New Jersey, Geneva, and Tokyo.