Short abstract

Synthetic heart data are claimed to boost the performance of predictive models. This presentation discusses the epistemic implications of how synthetic data is experimented with and used for heart failure prediction and what it conveys about synthetic representations of the normal and pathological.

Long abstract

From a single heartbeat, computational models are used to extrapolate consecutive beats for ECG recordings. This is one example of how data about the heart is generated by computation. To improve and personalize the prediction of risk and prognosis of cardiovascular diseases is also a common motivation for using synthetic electronic health records, seen as increasing variability and enlarging training datasets to develop better AI models. In this presentation, I discuss the epistemic and representational implications of how synthetic data is experimented with and used to boost the performance of heart failure prediction.

Studies suggest that AI models trained wholly or partially on synthetic ECG data can outperform models trained exclusively on real world recordings, by enhancing generalizability, reducing overfitting, and compensating for class imbalance. A persistent challenge is evaluating the accuracy and clinical validity of the generated heartbeats. This raises the question of what is seen as good representations of human hearts, their functions, variations and pathologies?

Drawing on an ongoing ethnographic study, theories about the creation of normalcy and pathology (Canguilhem, 1989) and the politics of prediction (Amoore, 2013), I highlight practices of how the heart is represented by synthetic data in medical sciences and AI. By this, I make visible the epistemic considerations and implications of synthetic data hearts, heartbeats and patient data, for heart failure prediction and discuss what it conveys about how synthetic data are enacted as representations of the normal, pathological and risky heart.