ArticleIEEE transactions on bio-medical engineering2019
Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.Frontiers in digital health · 2022Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.JMIR formative research · 2026Article
- Cross-sectional stratification of fall-related vulnerability in Parkinson's disease through multimodal fusion of speech, gait, facial expression, and upper-limb movement.Frontiers in digital health · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech.Medical & biological engineering & computing · 2025Article
- Modernizing the Staging of Parkinson Disease Using Digital Health Technology.Journal of medical Internet research · 2025Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- AI-driven precision diagnosis and treatment in Parkinson's disease: a comprehensive review and experimental analysis.Frontiers in aging neuroscience · 2025Article
- Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation.JMIR rehabilitation and assistive technologies · 2024Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- Article
- A systematic review on artificial intelligence approaches for smart health devices.PeerJ. Computer science · 2024Review
- Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease.Scientific reports · 2023Article
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning.Sensors (Basel, Switzerland) · 2023Article
- End-to-End Deep Learning Method for Detection of Invasive Parkinson's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms.NPJ digital medicine · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) using smartphones. This contribution presents multi-source ensemble learning, a methodology which combines dataset deconstruction with ensemble learning and enables participants with incomplete data (i.e., where not all sensor data is available) to be included in the training of machine learning models and achieves a 100% participant retention rate. We demonstrate the proposed method on a cohort of 1513 participants, 91.2% of which contributed incomplete data in tapping, gait, voice, and/or memory tests. The use of multi-source ensemble learning, alongside convolutional neural networks (CNNs) capitalizing on the amount of available data, increases PD classification accuracy from 73.1% to 82.0% as compared to traditional techniques. The increase in accuracy is found to be partly caused by the use of multi-channel CNNs and partly caused by developing models using the large cohort of participants. Furthermore, through bootstrap sampling we reveal that feature selection is better performed on a large cohort of participants with incomplete data than on a small number of participants with complete data. The proposed method is applicable to a wide range of wearable/remote monitoring datasets that suffer from missing data and contributes to improving the ability to remotely monitor PD via revealing novel methods of accounting for symptom heterogeneity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.