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Remote assessment of Parkinson’s disease symptom severity based on group interaction feature assistance

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Abstract

Telemonitoring is an effective way to assess the severity of Parkinson's disease (PD). Due to heterogeneity and small sample sizes, the multi-task learning is applied to build the specific model for PD patients and prevent overfitting. However, the existing multi-task learning methods don't consider the nonlinear interaction between patients. Therefore, to improve the performance of the patient-specific prediction model, this paper proposes a group interaction feature assistance method (GIFA) for remote assessment of PD symptom severity. First, GIFA employs the nonlinear bidirectional long short-term memory network to explore the correlation among patient groups. Next, the incremental association Markov boundary is adopted to select causal features from group interaction features obtained by the bidirectional long short-term memory network to reduce negative transfer. Finally, the causal interaction features learned by the incremental association Markov boundary are input into the patient-specific prediction model to assist disease assessment, which is conducive to increasing the complementary information and improving the prediction performance. Experiment results on the public Parkinson's telemonitoring and mPower voice datasets show that GIFA model outperforms the cited state-of-the-art comparison methods for predicting Parkinson's disease symptom severity.

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Data availability

The datasets generated during and/or analysed during the current study are available in the UC Irvine Machine Learning repository (https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring) and the mPower Public Researcher Portal (https://www.synapse.org/#!Synapse:syn5511444/tables/).

Code availability

The code is available at https://github.com/ysutaoteam/GIFA.

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Funding

This work was supported by the National Natural Science Foundation of China under Grant 62176229, the Natural Science Foundation of Hebei Province under Grant H2021203002, and Postgraduate Innovation Fund Project of Hebei Province under Grant CXZZBS2023046.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, China

    Zaifa Xue, Huibin Lu, Tao Zhang, Xiaonan Guo & Le Gao

  2. Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, China

    Zaifa Xue, Huibin Lu, Tao Zhang, Xiaonan Guo & Le Gao

Authors
  1. Zaifa Xue
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  2. Huibin Lu
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  3. Tao Zhang
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  4. Xiaonan Guo
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  5. Le Gao
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Contributions

ZX: Investigation, Methodology, Writing-original draft, Validation. HL: Project administration, Supervision, Writing-review and editing. TZ: Conceptualization, Methodology, Funding acquisition, Project administration, Supervision. XG: Validation, Funding acquisition, Writing-review and editing. LG: Methodology, Writing-review and editing.

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Correspondence to Tao Zhang.

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Xue, Z., Lu, H., Zhang, T. et al. Remote assessment of Parkinson’s disease symptom severity based on group interaction feature assistance. Int. J. Mach. Learn. & Cyber. (2023). https://doi.org/10.1007/s13042-023-02050-x

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