Widespread adoption of AI for medical decision making is still hindered due
to ethical and safety-related concerns. For AI-based decision support systems
in healthcare settings it is paramount to be reliable and trustworthy. Common
deep learning approaches, however, have the tendency towards overconfidence
under data shift. Such inappropriate extrapolation beyond evidence-based
scenarios may have dire consequences. This highlights the importance of
reliable estimation of local uncertainty and its communication to the end user.
While stochastic neural networks have been heralded as a potential solution to
these issues, this study investigates their actual reliability in clinical
applications. We centered our analysis on the exemplary use case of mortality
prediction for ICU hospitalizations using EHR from MIMIC3 study. For
predictions on the EHR time series, Encoder-Only Transformer models were
employed. Stochasticity of model functions was achieved by incorporating common
methods such as Bayesian neural network layers and model ensembles. Our models
achieve state of the art performance in terms of discrimination performance
(AUC ROC: 0.868+-0.011, AUC PR: 0.554+-0.034) and calibration on the mortality
prediction benchmark. However, epistemic uncertainty is critically
underestimated by the selected stochastic deep learning methods. A heuristic
proof for the responsible collapse of the posterior distribution is provided.
Our findings reveal the inadequacy of commonly used stochastic deep learning
approaches to reliably recognize OoD samples. In both methods, unsubstantiated
model confidence is not prevented due to strongly biased functional posteriors,
rendering them inappropriate for reliable clinical decision support. This
highlights the need for approaches with more strictly enforced or inherent
distance-awareness to known data points, e.g., using kernel-based techniques.

采用 AI 进行医疗决策仍面临伦理和安全方面的问题。研究表明常用的深度学习方法对数据漂移过于自信，却未能在临床应用中可靠地识别异常样本，暴露了其可靠性不足的问题。存在着更加严格或内生于已知数据点的方法的需求，例如使用基于核的技术。