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