Outcome Prediction from Behaviour Change Intervention Evaluations using a Combination of Node and Word Embedding

Debasis Ganguly, Martin Gleize, Yufang Hou, Charles Jochim, Francesca Bonin, Alessandra Pascale, Pierpaolo Tommasi, Pol Mac Aonghusa, Robert West, Marie Johnston, Mike Kelly, Susan Michie

Research output: Contribution to journalConference articlepeer-review

Abstract

Findings from randomized controlled trials (RCTs) of behaviour change interventions encode much of our knowledge on intervention efficacy under defined conditions. Predicting outcomes of novel interventions in novel conditions can be challenging, as can predicting differences in outcomes between different interventions or different conditions. To predict outcomes from RCTs, we propose a generic framework of combining the information from two sources - i) the instances (comprised of surrounding text and their numeric values) of relevant attributes, namely the intervention, setting and population characteristics of a study, and ii) abstract representation of the categories of these attributes themselves. We demonstrate that this way of encoding both the information about an attribute and its value when used as an embedding layer within a standard deep sequence modeling setup improves the outcome prediction effectiveness.

Original languageEnglish
Pages (from-to)486-495
Number of pages10
JournalAMIA Annual Symposium Proceedings
Volume2021
DOIs
Publication statusPublished - 2021

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