ArticleFrontiers in medicine2023
Early detection of Parkinson's disease through enriching the electronic health record using a biomedical knowledge graph.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Engagement with the national electronic health records by people with Parkinson's disease.Frontiers in aging neuroscience · 2026Article
- The Barancik award lecture: Multi-disciplinary research will be the key to stop, restore, and end MS.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
- Precision Drug Repurposing (PDR): Patient-level modeling and prediction combining foundational knowledge graph with biobank data.Journal of biomedical informatics · 2025Article
- Connecting electronic health records to a biomedical knowledge graph to link clinical phenotypes and molecular endotypes in atopic dermatitis.Scientific reports · 2025Article
- Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics.Frontiers in neurology · 2025Review
- Knowledge graph and its application in the study of neurological and mental disorders.Frontiers in psychiatry · 2025Review
- Knowledge of Parkinson's disease among non-PD specialists: a web-based survey in South China.Frontiers in aging neuroscience · 2025Article
- Artificial intelligence in food and nutrition evidence: The challenges and opportunities.PNAS nexus · 2024Article
- Biomedical knowledge graph-optimized prompt generation for large language models.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
- Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs.Frontiers in medicine · 2023Review
- Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Early diagnosis of Parkinson's disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR). Methods: To predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group. Results: The classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification. Discussion: The proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence.
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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.