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

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 prediction...

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Detalles Bibliográficos
Autores principales: Popat, Ram, Ive, Julia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508184/
https://www.ncbi.nlm.nih.gov/pubmed/37731823
http://dx.doi.org/10.3389/fdgth.2023.1188338
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author Popat, Ram
Ive, Julia
author_facet Popat, Ram
Ive, Julia
author_sort Popat, Ram
collection PubMed
description 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 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.
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spelling pubmed-105081842023-09-20 Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions Popat, Ram Ive, Julia Front Digit Health Digital Health 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 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. Frontiers Media S.A. 2023-09-05 /pmc/articles/PMC10508184/ /pubmed/37731823 http://dx.doi.org/10.3389/fdgth.2023.1188338 Text en © 2023 Popat and Ive. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Digital Health
Popat, Ram
Ive, Julia
Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions
title Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions
title_full Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions
title_fullStr Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions
title_full_unstemmed Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions
title_short Embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions
title_sort embracing the uncertainty in human–machine collaboration to support clinical decision-making for mental health conditions
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508184/
https://www.ncbi.nlm.nih.gov/pubmed/37731823
http://dx.doi.org/10.3389/fdgth.2023.1188338
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