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Toward Learning Machines at a Mother and Baby Unit

Agnostic analyses of unique video material from a Mother and Baby Unit were carried out to investigate the usefulness of such analyses to the unit. The goal was to improve outcomes: the health of mothers and their babies. The method was to implement a learning machine that becomes more useful over t...

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Autores principales: Boman, Magnus, Downs, Johnny, Karali, Abubakrelsedik, Pawlby, Susan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691596/
https://www.ncbi.nlm.nih.gov/pubmed/33281668
http://dx.doi.org/10.3389/fpsyg.2020.567310
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author Boman, Magnus
Downs, Johnny
Karali, Abubakrelsedik
Pawlby, Susan
author_facet Boman, Magnus
Downs, Johnny
Karali, Abubakrelsedik
Pawlby, Susan
author_sort Boman, Magnus
collection PubMed
description Agnostic analyses of unique video material from a Mother and Baby Unit were carried out to investigate the usefulness of such analyses to the unit. The goal was to improve outcomes: the health of mothers and their babies. The method was to implement a learning machine that becomes more useful over time and over task. A feasible set-up is here described, with the purpose of producing intelligible and useful results to healthcare professionals at the unit by means of a vision processing pipeline, grouped together with multi-modal capabilities of handling annotations and audio. Algorithmic bias turned out to be an obstacle that could only partly be handled by modern pipelines for automated feature analysis. The professional use of complex quantitative scoring for various mental health-related assessments further complicated the automation of laborious tasks. Activities during the MBU stay had previously been shown to decrease psychiatric symptoms across diagnostic groups. The implementation and first set of experiments on a learning machine for the unit produced the first steps toward explaining why this is so, in turn enabling decision support to staff about what to do more and what to do less of.
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spelling pubmed-76915962020-12-04 Toward Learning Machines at a Mother and Baby Unit Boman, Magnus Downs, Johnny Karali, Abubakrelsedik Pawlby, Susan Front Psychol Psychology Agnostic analyses of unique video material from a Mother and Baby Unit were carried out to investigate the usefulness of such analyses to the unit. The goal was to improve outcomes: the health of mothers and their babies. The method was to implement a learning machine that becomes more useful over time and over task. A feasible set-up is here described, with the purpose of producing intelligible and useful results to healthcare professionals at the unit by means of a vision processing pipeline, grouped together with multi-modal capabilities of handling annotations and audio. Algorithmic bias turned out to be an obstacle that could only partly be handled by modern pipelines for automated feature analysis. The professional use of complex quantitative scoring for various mental health-related assessments further complicated the automation of laborious tasks. Activities during the MBU stay had previously been shown to decrease psychiatric symptoms across diagnostic groups. The implementation and first set of experiments on a learning machine for the unit produced the first steps toward explaining why this is so, in turn enabling decision support to staff about what to do more and what to do less of. Frontiers Media S.A. 2020-11-13 /pmc/articles/PMC7691596/ /pubmed/33281668 http://dx.doi.org/10.3389/fpsyg.2020.567310 Text en Copyright © 2020 Boman, Downs, Karali and Pawlby. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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 Psychology
Boman, Magnus
Downs, Johnny
Karali, Abubakrelsedik
Pawlby, Susan
Toward Learning Machines at a Mother and Baby Unit
title Toward Learning Machines at a Mother and Baby Unit
title_full Toward Learning Machines at a Mother and Baby Unit
title_fullStr Toward Learning Machines at a Mother and Baby Unit
title_full_unstemmed Toward Learning Machines at a Mother and Baby Unit
title_short Toward Learning Machines at a Mother and Baby Unit
title_sort toward learning machines at a mother and baby unit
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691596/
https://www.ncbi.nlm.nih.gov/pubmed/33281668
http://dx.doi.org/10.3389/fpsyg.2020.567310
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