Toward Learning Machines at a Mother and Baby Unit.
Toward Learning Machines at a Mother and Baby Unit.
复制标题
DOI：
10.3389/fpsyg.2020.567310
复制
发表时间：
2020
影响因子：
3.8
通讯作者：
Pawlby S
中科院分区：
文献类型：
--
作者：
Boman M;Downs J;Karali A;Pawlby S
从母亲和婴儿股独特的视频材料进行了不可知论分析，以调查这种分析的有用性。目标是改善结果：母亲及其婴儿的健康。该方法是实现一个学习机器，随着时间和任务的推移变得更加有用。这里描述了一种可行的设置，其目的是通过视觉处理管道为单位的医疗保健专业人员产生可理解和有用的结果，该管道与处理注释和音频的多模式功能组合在一起。结果证明，数学偏差是一个障碍，现代管道只能部分处理自动特征分析。对各种与心理健康有关的评估进行复杂的定量评分的专业使用进一步使繁重任务的自动化复杂化。在MBU停留期间的活动先前已被证明可以减少诊断组的精神症状。该单位在学习机上的实施和第一组实验为解释为什么会这样迈出了第一步，从而为员工提供决策支持，帮助他们了解哪些工作应该做得更多，哪些工作应该做得更少。
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.
登录
查看更多内容
影响因子：
4.8
作者：
Kenny, Maeve;Conroy, Susan;Pawlby, Susan
通讯作者：
Pawlby, Susan
影响因子：
10.5
作者：
Leucht, S;Kane, JM;Engel, R
通讯作者：
Engel, R
DOI：
10.1109/tpami.2013.23
发表时间：
2013-12-01
影响因子：
23.6
作者：
Belhumeur, Peter N.;Jacobs, David W.;Kumar, Neeraj
通讯作者：
Kumar, Neeraj
影响因子：
2.9
作者：
Boman, Magnus;Sanches, Pedro
通讯作者：
Sanches, Pedro
影响因子：
11.2
作者：
Martinez, Brais;Valstar, Michel F.;Pantic, Maja
通讯作者：
Pantic, Maja