Automatic structure estimation of predictive models for symptom development
Abstract
Online mental health treatment has the premise to meet the increasing demand for mental health treatment at a lower cost than traditional treatment. However, online treatment suffers from high drop-out rates, which might negate their cost effectiveness. Predictive models might aid in early identification of deviating clients which allows to target them directly to prevent drop-out and improve treatment outcomes. We propose a two-staged multi-objective optimization process to automatically infer model structures based on ecological momentary assessment for prediction of future symptom development. The proposed multi-objective optimization approach results in a temporal-causal network model with the best prediction performance for each concept. This allows for a selection of a disorder-specific model structure based on the envisioned field of application.
Cite
@article{arxiv.1809.04494,
title = {Automatic structure estimation of predictive models for symptom development},
author = {Dennis Becker},
journal= {arXiv preprint arXiv:1809.04494},
year = {2018}
}