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


Type
Article
Change log
Authors
Ganguly, Debasis 
Gleize, Martin 
Hou, Yufang 
Jochim, Charles 
Bonin, Francesca 
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.

Description
Keywords
Humans, Knowledge, Prognosis, Text Messaging
Journal Title
AMIA Annu Symp Proc
Conference Name
Journal ISSN
1531-605X
1942-597X
Volume Title
2021
Publisher
Publisher DOI
Publisher URL