King's College London

Research portal

Learning Machines at a Mother and Baby Unit

Research output: Contribution to journalArticle

Magnus Boman, Johnny Downs, Abubakrelsedik Karali, Susan Pawlby

Original languageEnglish
JournalFrontiers in Psychology
Accepted/In press12 Oct 2020

King's Authors

Abstract

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 towards explaining why this is so, in turn enabling decision support to staff about what to do more and what to do less of.

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454