Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach

Sophie Anne Inès Klopfenstein   ^{1, 2} , MD ;   Anne Rike Flint   ¹ , MSc ;   Patrick Heeren   ^{1, 3} , BSc ;   Mona Prendke   ¹ , BSc, MD ;   Amin Chaoui   ¹ , MD ;   Thomas Ocker   ³ , Dr med, MD ;   Jonas Chromik   ⁴ , MSc ;   Bert Arnrich   ⁴ , Prof Dr ;   Felix Balzer   ^{1, 5 *} , MSc, MD, Dr rer nat, Prof Dr Med ;   Akira-Sebastian Poncette   ^{1, 3 *} , MD, Prof Dr Med

¹ Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany

² Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany

³ Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany

⁴ Digital Health - Connected Healthcare, Hasso-Plattner-Institute, University of Potsdam, Potsdam, Germany

⁵ Einstein Center Digital Future, Berlin, Germany

*these authors contributed equally