Digital Biomarkers in Parkinson's Disease

Polish-Japanese Academy of Information Technology

  1. Classification of Parkinson’s Disease Using Machine Learning with MoCA Response Dynamics
    Chudzik, Artur, and Andrzej W. Przybyszewski. Classification of Parkinson’s Disease Using Machine Learning with MoCA Response Dynamics. Applied Sciences 14.7 (2024): 2979. https://doi.org/10.3390/app14072979
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  2. Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases
    Chudzik A, Śledzianowski A, Przybyszewski AW. Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases. Sensors. 2024; 24(5):1572. https://doi.org/10.3390/s24051572
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  3. Investigating the Impact of Parkinson’s Disease on Brain Computations: An Online Study of Healthy Controls and PD Patients.
    Chudzik, Artur, Aldona Drabik, and Andrzej W. Przybyszewski. Asian Conference on Intelligent Information and Database Systems. Singapore: Springer Nature Singapore, 2023.
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  4. Universal Machine-Learning Processing Pattern for Computing in the Video-Oculography
    Śledzianowski, Albert, et al. International Conference on Computational Science. Cham: Springer Nature Switzerland, 2023.
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  5. Detecting True and Declarative Facial Emotions by Changes in Nonlinear Dynamics of Eye Movements
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  6. Comparison of Different Data Mining Methods to Determine Disease Progression in Dissimilar Groups of Parkinson's Patients
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  7. Parkinson's disease development prediction by c-granule computing compared to different AI methods
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  8. IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson's Disease Patients
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  9. Eye-Tracking and Machine Learning Significance in Parkinson's Disease Symptoms Prediction
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  10. Combining Results of Different Oculometric Tests Improved Prediction of Parkinson's Disease Development
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  11. Building Classifiers for Parkinson's Disease Using New Eye Tribe Tracking Method
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  12. Evaluating reflexive saccades and UDPRS as markers of Deep Brain Stimulation and Best Medical Treatment improvements in Parkinson's disease patients: a prospective controlled study
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  13. Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson's Disease Patients
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  14. Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson's Disease Progression
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  15. Parkinson's Disease Development Prediction by C-Granule Computing
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  16. Algorithms for computing indexes of neurological gait abnormalities in patients after DBS surgery for Parkinson Disease based on motion capture data
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  17. Data Mining and Machine Learning on the Basis from Reflexive Eye Movements Can Predict Symptom Development in Individual Parkinson's Patients
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  18. Rough Set Based Classifications of Parkinson's Patients Gaits
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  19. Machine Learning on the Video Basis of Slow Pursuit Eye Movements Can Predict Symptom Development in Parkinson's Patients
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  20. Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients
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  21. Rough Set Rules Determine Disease Progressions in Different Groups of Parkinson's Patients
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  22. Multimodal Learning Determines Rules of Disease Development in Longitudinal Course with Parkinson's Patients
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  23. Fuzzy RST and RST Rules Can Predict Effects of Different Therapies in Parkinson's Disease Patients
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  24. The Neuromodulatory Impact of Subthalamic Nucleus Deep Brain Stimulation on Gait and Postural Instability in Parkinson's Disease Patients: A Prospective Case Controlled Study
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  25. Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson's Disease Patients
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  26. Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson's Disease Progression
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