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Personalising Digital Health Behaviour Change Interventions using Machine Learning and Domain Knowledge

arXiv 2304.03392 ↗First posted 6 Apr 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behaviour and uses counterfactual examples with feature control to guide personalisation of BCI. We use simulated patient data with varying levels of receptivity to intervention to arrive at the study design which would enable evaluation of our system.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 9 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

2 of 9 references.

References · 2

  1. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 614
  2. 2022What do you need to consider when designing mobile health intervention?cited by 2

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