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Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12323))

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Abstract

Bipolar Disorder (BD) is a chronic mental illness characterized by changing episodes from euthymia (healthy state) through depression and mania to the mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Previous research on predicting a new BD episode use mostly supervised learning methods that require labeled data and hence force a filtering of the available data to retain only those data that have valid labels (from the psychiatric assessment). To avoid limitations of supervised learning, in this paper we investigate the use of a semi-supervised learning approach that combines both labeled and unlabeled data to derive a model for BD episode prediction. Specifically we apply the DISSFCM (Dynamic Incremental Semi-Supervised Fuzzy C-Means) algorithm which offers the possibility to process in an incremental fashion the data stream of the voice signal captured by the smartphone, thus exploiting the evolving time structure of data which is ignored by static learning methods. DISSFCM processes data in form of chunks and creates a dynamic collection of clusters thanks to a splitting mechanism that generates new clusters to better capture the hidden geometrical structure of data. This gives DISSFCM the ability to detect changes in data and dynamically adapt the model to them, thus improving the prediction accuracy. Preliminary results on real-world data collected at the Department of Affective Disorders, Institute of Psychiatry and Neurology in Warsaw (Poland) show that DISSFCM is able to predict some of healthy episodes (euthymia) and disease episodes even when only 25% of labeled data are available. Moreover DISSFM performs better than its previous version without split (ISSFCM) and it also overcomes the batch algorithm (SSFCM) that uses the whole dataset to create the model.

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Notes

  1. 1.

    Global Strategy on Digital Health 2020–2024 https://extranet.who.int/.

  2. 2.

    Data considered in this paper come from CHAD project − entitled “Smartphone-based diagnostics of phase changes in the course of bipolar disorder” (RPMA.01.02.00-14-5706/16-00) that was financed from EU funds (Regional Operational Program for Mazovia) in 2017–2018.

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Acknowledgment

Datasets considered in this paper were collected in the CHAD project − entitled “Smartphone-based diagnostics of phase changes in the course of bipolar disorder” (RPMA.01.02.00-14-5706/16-00) that was financed from EU funds (Regional Operational Program for Mazovia) in 2017–2018. The authors thank psychiatrists and patients that participated in the observational study for their commitment. The authors thank the researchers Olga Kamińska, Karol Opara and Weronika Radziszewska from Systems Research Institute, Polish Academy of Sciences for their support in data preparation and analysis, as well as the researchers Monika Dominiak, Anna Antosik-Wójcińska and Łukasz Świecicki from Institute of Psychiatry and Neurology for their advice and comments. This work has been partially supported by the GNCS-INDAM (Gruppo Nazionale per il Calcolo Scientifico of Istituto Nazionale di Alta Matematica) within the research project “Computational Intelligence methods for Digital Health”.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bari, Bari, Italy

    Gabriella Casalino, Giovanna Castellano & Francesco Galetta

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Katarzyna Kaczmarek-Majer

Authors
  1. Gabriella Casalino
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  2. Giovanna Castellano
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  3. Francesco Galetta
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  4. Katarzyna Kaczmarek-Majer
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Corresponding author

Correspondence to Gabriella Casalino .

Editor information

Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

  3. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  4. Dalhousie University, Halifax, NS, Canada

    Stan Matwin

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Casalino, G., Castellano, G., Galetta, F., Kaczmarek-Majer, K. (2020). Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-61527-7_6

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