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Multi-view Approach to Parkinson’s Disease Quality of Life Data Analysis

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New Frontiers in Mining Complex Patterns (NFMCP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10312))

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Abstract

Parkinson’s disease is a neurodegenerative disorder that affects people worldwide. While the motor symptoms such as tremor, rigidity, bradykinesia and postural instability are predominant, patients experience also non-motor symptoms, such as decline of cognitive abilities, behavioural problems, sleep disturbances, and other symptoms that greatly affect their quality of life. Careful management of patient’s condition is crucial to ensure the patient’s independence and the best possible quality of life. This is achieved by personalized medication treatment based on individual patient’s symptoms and medical history. This paper explores the utility of machine learning to help development of decision models, aimed to support clinicians’ decisions regarding patients’ therapies. We propose a new multi-view methodology for determining groups of patients with similar symptoms and detecting patterns of medications changes that lead to the improvement or decline of patients’ quality of life. We identify groups of patients ordered in accordance to their quality of life assessment and find examples of therapy modifications which induce positive or negative change of patients’ symptoms. The results demonstrate that motor and autonomic symptoms are the most informative for evaluating the quality of life of Parkinson’s disease patients.

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Notes

  1. 1.

    Data used in the preparation of this article were obtained from the Parkinsons Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners. List of funding partners can be found at www.ppmi-info.org/fundingpartners.

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Acknowledgements

This work was supported by the PD_manager project, funded within the EU Framework Programme for Research and Innovation Horizon 2020 grant 643706. We acknowledge the support of the Slovenian Research Agency and the European Commission through The Human Brain Project (HBP), grant FP7-ICT-604102.

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Authors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Anita Valmarska, Dragana Miljkovic & Nada Lavrač

  2. Jožef Stefan International Postgraduate School, Ljubljana, Slovenia

    Anita Valmarska & Nada Lavrač

  3. Faculty of Computer and Information Science, Ljubljana, Slovenia

    Marko Robnik-Šikonja

Authors
  1. Anita Valmarska
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  2. Dragana Miljkovic
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  3. Marko Robnik-Šikonja
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  4. Nada Lavrač
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Corresponding author

Correspondence to Anita Valmarska .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR-CNR, Rende, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Valmarska, A., Miljkovic, D., Robnik-Šikonja, M., Lavrač, N. (2017). Multi-view Approach to Parkinson’s Disease Quality of Life Data Analysis. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-61461-8_11

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