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While it has been well known in the ML community that deep learning modelssuffer from instability, the consequences for healthcare deployments are undercharacterised. We study the stability of different model architectures trainedon electronic health records, using a set of outpatient prediction tasks as acase study. We show that repeated training runs of the same deep learning modelon the same training data can result in significantly different outcomes at apatient level even though global performance metrics remain stable. We proposetwo stability metrics for measuring the effect of randomness of model training,as well as mitigation strategies for improving model stability.

Instability in clinical risk stratification models using deep learning