Early detection of Parkinson's disease through enriching the electronic health record using a biomedical knowledge graph
| Autor: | Karthik Soman, Charlotte A. Nelson, Gabriel Cerono, Samuel M. Goldman, Sergio E. Baranzini, Ethan G. Brown |
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| Rok vydání: | 2023 |
| Předmět: |
Aging
screening and diagnosis Parkinson's Disease Neurosciences General Medicine electronic health record Neurodegenerative Brain Disorders 4.1 Discovery and preclinical testing of markers and technologies Parkinson disease Detection machine learning Good Health and Well Being knowledge graph Networking and Information Technology R&D (NITRD) Clinical Research graph algorithm Neurological neurodegenerative disorder |
| Popis: | IntroductionEarly 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).MethodsTo 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.ResultsThe 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.DiscussionThe 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. |
| Databáze: | OpenAIRE |
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