ArticleScientific reports2018
Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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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
62 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The identification of cognitive impairment in Parkinson's disease using biofluids, neuroimaging, and artificial intelligence.Frontiers in neuroscience · 2024Pooled it
- Multimodal assessment of balance dysfunction in older adults: from classification to clustering-based functional pattern identification.Journal of neuroengineering and rehabilitation · 2026Article
- Safety of Early Rehabilitation for Acute Heart Failure Using Japanese Medical Claims.Journal of the American Heart Association · 2026Article
- Classification of fall risk in Parkinson's disease using empirical mode decomposition and machine learning.Frontiers in bioengineering and biotechnology · 2026Article
- Non-motor symptoms as critical predictors of quality of life in Parkinson's disease: a machine learning approach.Health and quality of life outcomes · 2025Observational
- Effects of Levodopa on Gait in People With Parkinson's Disease With and Without a History of Falls.Movement disorders clinical practice · 2025Article
- Machine learning approaches to identify neonates and young children at risk for postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia.BMJ paediatrics open · 2025Observational
- Modeling and optimizing deep brain stimulation to enhance gait in Parkinson's disease: personalized treatment with neurophysiological insights.NPJ Parkinson's disease · 2025Article
- Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data.Genomics, proteomics & bioinformatics · 2025Article
- Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.European journal of neurology · 2025Observational
- Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson's Disease from Other Forms of Parkinsonism.Biomedicines · 2025Article
- Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning.Scientific reports · 2025Article
- Decoding event-related potentials: single-dose energy dietary supplement acts on earlier brain processes than we thought.Frontiers in neuroinformatics · 2025Article
- Risk factors associated with severe progression of Parkinson's disease: random forest and logistic regression models.Frontiers in neurology · 2025Article
- Gut-brain axis modulation in remote rehabilitation of Parkinson's disease: reconstructing the fecal metabolome and nigral network connectivity.Frontiers in neurology · 2025Review
- Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.Scientific reports · 2024Article
- Predicting Quality of Life in Parkinson's Disease: A Machine Learning Approach Employing Common Clinical Variables.Journal of clinical medicine · 2024Article
- Gait Alterations and Association With Worsening Knee Pain and Physical Function: A Machine Learning Approach With Wearable Sensors in the Multicenter Osteoarthritis Study.Arthritis care & research · 2024Observational
- Severity Analysis for Occupational Heat-related Injury Using the Multinomial Logit Model.Safety and health at work · 2024Article
- Modeling Epidemiology Data with Machine Learning Technique to Detect Risk Factors for Gastric Cancer.Journal of gastrointestinal cancer · 2024Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
Abstract
In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70-80%.
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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.