Embracing the uncertainty in human-machine collaboration to support clinical decision-making for mental health conditions.

ABSTRACT: Two significant obstacles exist preventing the widespread usage of Deep Learning (DL) models for predicting healthcare outcomes in general and mental health conditions 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 clos

SUBMITTER: Popat R 

PROVIDER: S-EPMC10508184 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature