Artificial Intelligence-Assisted Semi-automated Personal Health Data Curation and Reuse for Myocardial Infarction
Wenjie Liang, Rutger van Mierlo, Anne-Lore Bynens, Eno Martin Lotman, Markus Kreuzthaler, Martin Benedikt, Ensar Erol, Omer Durukan Kilic, Katerina Serafimova, Todor Primov, Svetla Boytcheva, Cecile J A Wolfs, Aiara Lobo Gomes, Remzi Celebi, Isabelle de Zegher, Andre Dekker, Petros Kalendralis, , Alexander Kreutz, Alpár Tana, Aranka Ravai-Nagy, Bart Scheenstra, Béla Bihari, Botond Kiss, Dominik Steiger, Elion Sõber, Emmanuel Benoist, Gleb Klimenkov, Gregorio Sambataro, Heimo Müller, Stefan Schulz, Michaela Kargl, Markus Plass, Mall Maasik, István Horváth, János Dániel Dallos, Katrin Lepik, Kristian Kankainen, Liesbeth Boersma, Lóránt Ferencz, Michel Dumontier, Monika Moga, Sina Amirrajab, Zoltán Lázár, Zsolt MátéAbstract
Background and Aims
Digital applications have high potential in myocardial infarction management. This study aims to harmonize personal health data using a workflow driven by an Artificial Intelligence-assisted virtual assistant prototype, then reuse the data to facilitate the creation of standardized patient summaries and risk assessment.
Methods
The required configurations for data documentation and curation were developed to drive the workflow execution. With consent and data transfer agreements, data of patients with myocardial infarction were extracted from multi-source medical records, structured and ingested into the workflow. After transformation, personal health data were mapped into concrete clinical concepts connected with specific relationships defined by the reference ontology, forming source health knowledge graphs as the basic structure of personal health knowledge graphs. Then the constructed graphs were validated by using Shapes Constraint Language-based constraints, triggering the human-in-the-loop process to enhance data quality. Information queries were conducted for data reuse.
Results
38 out of 45 planned patients were recruited with a signed informed consent. Thirty patients finalized the study. Overall successful data ingestion rate is approximately 83%. The average data quality score is 95.45/100. Constructed personal health knowledge graphs can be reused to represent medical information as an international patient summary and evaluate individual risk of future cardiovascular events by calculating the Second Manifestations of Arterial Disease risk score.
Conclusion
Our proposed workflow is feasible for semi-automated personal health data curation and secondary use. It provides a potential solution to enhance medical data interoperability, thereby facilitating risk assessment in cardiovascular diseases.