Detection and presentation of bleeding events in the electronic health record

Aim

The goal of this project was to develop AI algorithms based on Natural Language Processing to find bleeding episodes reported in the patients' electronic health records. It was developed based on extraction of relevant text from more than 300,000 electronic health records (unstructured data). When implemented, the algorithm could extract information on bleeding episodes from the patient's medical record and the results would be reported to the medical doctor and patient in an easily understandable and relevant way.

RESULTS

The first part of the project proved that an artificial neural network can detect 98% of the bleeding episodes, and the second part of the project aimed to develop a viable solution which could become part of daily clinical practice. The solution should be able to find indications of bleeding episodes, present a summary for the clinician, and suggest appropriate measures.

Participants

• Department of Clinical Biochemistry, OUH
• Maersk Mc-Kinney Moller Institute, SDU

This project was a subproject of the project The Intelligent Health Record, which included several other subprojects on the use of AI to find information in the electronic health record.

Read more about the project The Intelligent Health Record at ipj.nu (website only available in Danish).