Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions - Popat, Ram; Ive, Julia (Frontiers in Digital Health, 2023)
Two significant obstacles exist preventing the widespread usage of Deep Learning (DL) models for predicting healthcare outcomes in general and Mental Health Conditions (MHCs) in particular. Firstly, DL models do not quantify the uncertainty in their predictions, so clinicians are unsure of which predictions they can trust. Secondly, DL models do not triage, i.e. separate which cases could be best handled by the human or the model. This paper attempts to address these obstacles using Bayesian Deep Learning (BDL), which extends DL probabilistically and allows us to quantify the model’s uncertainty, which we use to improve human-model collaboration. We implement a range of state-of-the-art DL models for Natural Language Processing and apply a range of BDL methods to these models. Taking a step closer to the real-life scenarios of human-AI collaboration, we propose a Referral Learning methodology for the models that make predictions for certain instances while referring the rest of the instances to a human expert for further assessment. The study demonstrates that models can significantly enhance their performance by seeking human assistance in cases where the model exhibits high uncertainty which is closely linked to misclassifications. Referral Learning offers two options: (1) supporting humans in cases where the model predicts with certainty, and (2) triaging cases where the model evaluated when it had a better chance of being right than the human by evaluating human disagreement. The latter method combines model uncertainty from BDL and human disagreement from multiple annotations, resulting in improved triaging capabilities.