Abstract
Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After examining the distributions of AUC scores in all cases, non-linear models appear to be superior to baseline linear models. Moreover, apart from personalized approaches, group-level prediction models are also likely to offer an enhanced performance. According to this, two different nomothetic approaches to integrate data of more than one individuals are examined, one using directly all data during training and one based on knowledge distillation. Interestingly, it is observed that in one of the two real-world datasets, knowledge distillation method achieves improved AUC scores (mean relative change of +17% compared to personalized) showing how it can benefit EMA data classification and performance.
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References
Boh, B., et al.: An Ecological Momentary Intervention for weight loss and healthy eating via smartphone and Internet: study protocol for a randomised controlled trial. Trials 17(1), 1–12 (2016)
Epskamp, S., et al.: Personalized network modeling in psychopathology: the importance of contemporaneous and temporal connections. Clin. Psychol. Sci. 6(3), 416–427 (2018)
Fried, E.I., van Borkulo, C.D., Cramer, A.O.J., Boschloo, L., Schoevers, R.A., Borsboom, D.: Mental disorders as networks of problems: a review of recent insights. Soc. Psychiatry Psychiatr. Epidemiol. 52(1), 1–10 (2016). https://doi.org/10.1007/s00127-016-1319-z
Fried, E.I., Cramer, A.O.: Moving forward: challenges and directions for psychopathological network theory and methodology. Perspect. Psychol. Sci. 12(6), 999–1020 (2017)
Fukui, S., Yu, J., Hashimoto, M.: Distilling knowledge for non-neural networks. In: 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1411–1416. IEEE (2019)
Haslbeck, J.M., Bringmann, L.F., Waldorp, L.J.: A tutorial on estimating time-varying vector autoregressive models. Multivar. Behav. Res. 56(1), 120–149 (2021)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
van der Krieke, L., et al.: Ecological momentary assessments and automated time series analysis to promote tailored health care: a proof-of-principle study. JMIR Res. Protoc. 4(3), e100 (2015)
Lou, Y., Caruana, R., Gehrke, J.: Intelligible models for classification and regression. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 150–158 (2012)
Lou, Y., Caruana, R., Gehrke, J., Hooker, G.: Accurate intelligible models with pairwise interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 623–631 (2013)
Myin-Germeys, I., et al.: Experience sampling methodology in mental health research: new insights and technical developments. World Psychiatry 17(2), 123–132 (2018)
Nori, H., Jenkins, S., Koch, P., Caruana, R.: InterpretML: a unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223 (2019)
Robinaugh, D.J., Hoekstra, R.H., Toner, E.R., Borsboom, D.: The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research. Psychol. Med. 50(3), 353 (2020)
Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32 (2008)
Soyster, P.D., Ashlock, L., Fisher, A.J.: Pooled and person-specific machine learning models for predicting future alcohol consumption, craving, and wanting to drink: a demonstration of parallel utility. Psychol. Addict. Behav. (2021). https://search.ebscohost.com/login.aspx?direct=true&db=pdh&AN=2021-38589-001&site=ehost-live&scope=site
Spanakis, G., Weiss, G., Boh, B., Roefs, A.: Network analysis of ecological momentary assessment data for monitoring and understanding eating behavior. In: Zheng, X., Zeng, D.D., Chen, H., Leischow, S.J. (eds.) ICSH 2015. LNCS, vol. 9545, pp. 43–54. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29175-8_5
Stamate, D., et al.: Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches. Schizophr. Res. 209, 156–163 (2019)
Wild, B., Eichler, M., Friederich, H.C., Hartmann, M., Zipfel, S., Herzog, W.: A graphical vector autoregressive modelling approach to the analysis of electronic diary data. BMC Med. Res. Methodol. 10(1), 1–13 (2010). https://doi.org/10.1186/1471-2288-10-28
Wright, A.G., Zimmermann, J.: Applied ambulatory assessment: integrating idiographic and nomothetic principles of measurement. Psychol. Assess. 31(12), 1467 (2019)
Acknowledgements
This study is part of the project “New Science of Mental Disorders” (www.nsmd.eu), supported by the Dutch Research Council and the Dutch Ministry of Education, Culture and Science (NWO gravitation grant number 024.004.016).
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Ntekouli, M., Spanakis, G., Waldorp, L., Roefs, A. (2022). Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham. https://doi.org/10.1007/978-3-031-01333-1_16
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