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Artificial Intelligence based on real-world data
Anne Torill Nordsletta, Director Health Analytics
Environmental and social media data
Electronic health records
Registries
Genomics
Medical imaging
Claims databases
Patient monitoring devices
Clinical data is unstructured
Clinical data is structured
• 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
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
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?
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
• 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