Digital Biomarkers in Parkinson's Disease

Polish-Japanese Academy of Information Technology

  1. 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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  2. 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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  3. Detecting True and Declarative Facial Emotions by Changes in Nonlinear Dynamics of Eye Movements
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  4. Comparison of Different Data Mining Methods to Determine Disease Progression in Dissimilar Groups of Parkinson's Patients
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  5. Parkinson's disease development prediction by c-granule computing compared to different AI methods
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  6. IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson's Disease Patients
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  7. Eye-Tracking and Machine Learning Significance in Parkinson's Disease Symptoms Prediction
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  8. Combining Results of Different Oculometric Tests Improved Prediction of Parkinson's Disease Development
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  9. Building Classifiers for Parkinson's Disease Using New Eye Tribe Tracking Method
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  10. 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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  11. Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson's Disease Patients
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  12. Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson's Disease Progression
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  13. Parkinson's Disease Development Prediction by C-Granule Computing
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  14. 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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  15. 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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  16. Rough Set Based Classifications of Parkinson's Patients Gaits
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  17. Machine Learning on the Video Basis of Slow Pursuit Eye Movements Can Predict Symptom Development in Parkinson's Patients
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  18. Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients
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  19. Rough Set Rules Determine Disease Progressions in Different Groups of Parkinson's Patients
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  20. Multimodal Learning Determines Rules of Disease Development in Longitudinal Course with Parkinson's Patients
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  21. Fuzzy RST and RST Rules Can Predict Effects of Different Therapies in Parkinson's Disease Patients
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  22. 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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  23. Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson's Disease Patients
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  24. Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson's Disease Progression
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