A two-step approach for the prediction of mood levels based on diary data
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
The analysis of diary data can increase insights into patients suffering from mental disorders and can help to personalize online interventions. We propose a two-step approach for such an analysis. We first categorize free text diary data into activity categories by applying a bag-of-words approach and explore recurrent neuronal networks to support this task. In a second step, we develop partial ordered logit models with varying levels of heterogeneity among clients to predict their mood. We estimate the parameters of these models by employing MCMC techniques and compare the models regarding their predictive performance. This two-step approach leads to an increased interpretability about the relationships between various activity categories and the individual mood level.
| Original language | English |
|---|---|
| Title of host publication | Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings : European Conference, ECML PKDD 2018, Dublin, Ireland |
| Editors | Ulf Brefeld, Edward Curry, Elizabeth Daly, Brian MacNamee, Alice Marascu, Fabio Pinelli, Michele Berlingerio, Neil Hurley |
| Number of pages | 4 |
| Place of Publication | Cham |
| Publisher | Springer International Publishing |
| Publication date | 2019 |
| Pages | 626-629 |
| ISBN (print) | 978-3-030-10996-7 |
| ISBN (electronic) | 978-3-030-10997-4 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2018 - Dublin, Ireland Duration: 10.09.2018 → 14.09.2018 http://www.ecmlpkdd2018.org/ |
- Business informatics - Text-mining, Ordinal logit, Diary data