Collaborative research in VRREG

Presenting AIR Lund: AIR Lund Chest Pain Substudy - and WP Core, Explainable clinical decision support based on health care register data.

The AIR Lund Chest Pain Substudy aims to develop decision support tools based on machine learning for the management of emergency department (ED) chest pain patients. For this purpose, we exploit the ESC-TROP register which contains extensive data from approximately 30 000 consecutive chest pain patients at the Skåne EDs in 2017-2018. As a first step, we have created an artificial neural network (ANN) to rule out acute myocardial infarction based on patient age, gender and two serial blood samples for troponin T. Preliminary results show that the ANN can increase the safe and early discharge of low risk chest pain patients from the ED compared with currently used decision rules. Future development steps include adding information to the ANN from e.g. the ECG and previous medical history.

The AIR Lund project also includes ethical and legal concerns with data-driven predictive tools. A challenge lies in making complex predictions explainable in the sense that they are sufficiently transparent for auditing and accountability as well as for being trustworthy in the context they are used in. The decision support tools we develop - particularly those providing high-stakes recommendations based on less intuitive models such as the above - will therefore also be assessed for explainability in relation to trust and accountability, which includes the relationship between the tool, its professional user and the patients.