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Artificial Intelligence based on real-world data
Anne Torill Nordsletta, Director Health Analytics
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Environmental and social media data
Electronic health records
Registries
Genomics
Medical imaging
Claims databases
Patient monitoring devices
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Clinical data is unstructured
Clinical data is structured
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• Predict anastomosis leakage• Early detection in pre-operativ
planning• Early warning and decision support• Previous study had a sensitivity of
100% and specificity was 72% withuse of bag-of-words model
What and why
Source: Ferris, Robert. Retrieved from https://www.slideshare.net/RobertFerris5/anastomotic-leak-following-colorectal-resection
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How and For What
Data available
NLP, statistics and machine learning
Prediction algorithm
Predict and identifyrisk patients
Colourbox.com
• Pre-operative planning, early warning and decision support.
• With improved specificityless expensive false alarms
Improve specificity
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Future work
Other clinical data
Data from otherclinics
At-home data
https://ehealthresearch.no/https://ehealthresearch.no/https://ehealthresearch.no/• Could real-world data from othersources contribute to the study?
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https://ehealthresearch.no/
CONTACT
Colourbox.com
Norwegian Centre for E-healthResearch
TromsøNorway
Anne Torill NordslettaDirector of Health Analytics
Norwegian Centre for E-health ResearchTromsø, Norway
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• Soguero-Ruiz, C., Hindberg, K., Rojo-Alvarez, J. L., Skrovseth, S. O., Godtliebsen, F., Mortensen, K., … Jenssen, R. (2016). Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records. IEEE Journal of Biomedical and Health Informatics, 20(5), 1404–1415. https://doi.org/10.1109/JBHI.2014.2361688
References