Standard
A two-step approach for the prediction of mood levels based on diary data. /
Bremer, Vincent; Becker, Dennis; Genz, Tobias et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings: European Conference, ECML PKDD 2018, Dublin, Ireland. ed. / Ulf Brefeld; Edward Curry; Elizabeth Daly; Brian MacNamee; Alice Marascu; Fabio Pinelli; Michele Berlingerio; Neil Hurley. Cham: Springer International Publishing AG, 2019. p. 626-629 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Bremer, V, Becker, D, Genz, T
, Funk, B & Lehr, D 2019,
A two-step approach for the prediction of mood levels based on diary data. in U Brefeld, E Curry, E Daly, B MacNamee, A Marascu, F Pinelli, M Berlingerio & N Hurley (eds),
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings: European Conference, ECML PKDD 2018, Dublin, Ireland. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11053 LNAI, Springer International Publishing AG, Cham, pp. 626-629, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2018, Dublin, Ireland,
10.09.18.
https://doi.org/10.1007/978-3-030-10997-4_39
APA
Bremer, V., Becker, D., Genz, T.
, Funk, B., & Lehr, D. (2019).
A two-step approach for the prediction of mood levels based on diary data. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, & N. Hurley (Eds.),
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings: European Conference, ECML PKDD 2018, Dublin, Ireland (pp. 626-629). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11053 LNAI). Springer International Publishing AG.
https://doi.org/10.1007/978-3-030-10997-4_39
Vancouver
Bremer V, Becker D, Genz T
, Funk B, Lehr D.
A two-step approach for the prediction of mood levels based on diary data. In Brefeld U, Curry E, Daly E, MacNamee B, Marascu A, Pinelli F, Berlingerio M, Hurley N, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings: European Conference, ECML PKDD 2018, Dublin, Ireland. Cham: Springer International Publishing AG. 2019. p. 626-629. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2019 Jan 15. doi: 10.1007/978-3-030-10997-4_39
Bibtex
@inbook{b51b9fce50f5409a9a33d630147de3c9,
title = "A two-step approach for the prediction of mood levels based on diary data",
abstract = "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.",
keywords = "Business informatics, Text-mining, Ordinal logit, Diary data",
author = "Vincent Bremer and Dennis Becker and Tobias Genz and Burkhardt Funk and Dirk Lehr",
note = "weiterer Autor: Tobias Genz, Institut f{\"u}r Wirtschaftsinformatik, Leuphana Universit{\"a}t L{\"u}neburg, Germany ; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2018, ECML PKDD 2018 ; Conference date: 10-09-2018 Through 14-09-2018",
year = "2019",
doi = "10.1007/978-3-030-10997-4_39",
language = "English",
isbn = "978-3-030-10996-7",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer International Publishing AG",
pages = "626--629",
editor = "Ulf Brefeld and Edward Curry and Elizabeth Daly and Brian MacNamee and Alice Marascu and Fabio Pinelli and Michele Berlingerio and Neil Hurley",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings",
address = "Switzerland",
url = "http://www.ecmlpkdd2018.org/",
}
RIS
TY - CHAP
T1 - A two-step approach for the prediction of mood levels based on diary data
AU - Bremer, Vincent
AU - Becker, Dennis
AU - Genz, Tobias
AU - Funk, Burkhardt
AU - Lehr, Dirk
N1 - weiterer Autor: Tobias Genz, Institut für Wirtschaftsinformatik, Leuphana Universität Lüneburg, Germany
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Business informatics
KW - Text-mining
KW - Ordinal logit
KW - Diary data
UR - http://www.scopus.com/inward/record.url?scp=85061126286&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-10997-4_39
DO - 10.1007/978-3-030-10997-4_39
M3 - Article in conference proceedings
SN - 978-3-030-10996-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 626
EP - 629
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
A2 - Brefeld, Ulf
A2 - Curry, Edward
A2 - Daly, Elizabeth
A2 - MacNamee, Brian
A2 - Marascu, Alice
A2 - Pinelli, Fabio
A2 - Berlingerio, Michele
A2 - Hurley, Neil
PB - Springer International Publishing AG
CY - Cham
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2018
Y2 - 10 September 2018 through 14 September 2018
ER -
