ArticleJournal of the American Medical Informatics Association : JAMIA2022
Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- A susceptibility network analysis of disease trajectories leading to multiple sclerosis: A nationwide cohort study.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- A phenotype-to-mechanism framework links phenome-wide comorbidity architecture to molecular mechanisms and therapeutic discovery in complex diseases.medRxiv : the preprint server for health sciences · 2026Article
- Inference of dependency knowledge graph for Electronic Health Records.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- Streamlining evidence based clinical recommendations with large language models.NPJ digital medicine · 2025Article
- Creating an Interactive Web Interface for Networks Stored in Knowledge Graph Databases.Current protocols · 2025Article
- A simple guide to the use of Student's t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics.BioData mining · 2025Article
- Phenotyping Healthcare Use 2-3 Decades Before the First Multiple Sclerosis Demyelinating Event.Annals of clinical and translational neurology · 2025Article
- KGG: a fully automated workflow for creating disease-specific knowledge graphs.Bioinformatics (Oxford, England) · 2025Article
- Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies.BMC medical research methodology · 2025Article
- The Barancik award lecture: Multi-disciplinary research will be the key to stop, restore, and end MS.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
- DOME: Directional medical embedding vectors from Electronic Health Records.Journal of biomedical informatics · 2025Article
- Biomedical knowledge graph-optimized prompt generation for large language models.Bioinformatics (Oxford, England) · 2024Article
- CoRTEx: contrastive learning for representing terms via explanations with applications on constructing biomedical knowledge graphs.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Harnessing EHR data for health research.Nature medicine · 2024Review
- Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis.Nature communications · 2024Article
- Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights.Nature aging · 2024Article
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveEarly identification of chronic diseases is a pillar of precision medicine as it can lead to improved outcomes, reduction of disease burden, and lower healthcare costs. Predictions of a patient's health trajectory have been improved through the application of machine learning approaches to electronic health records (EHRs). However, these methods have traditionally relied on "black box" algorithms that can process large amounts of data but are unable to incorporate domain knowledge, thus limiting their predictive and explanatory power. Here, we present a method for incorporating domain knowledge into clinical classifications by embedding individual patient data into a biomedical knowledge graph. MATERIALS AND
methodsA modified version of the Page rank algorithm was implemented to embed millions of deidentified EHRs into a biomedical knowledge graph (SPOKE). This resulted in high-dimensional, knowledge-guided patient health signatures (ie, SPOKEsigs) that were subsequently used as features in a random forest environment to classify patients at risk of developing a chronic disease.
resultsOur model predicted disease status of 5752 subjects 3 years before being diagnosed with multiple sclerosis (MS) (AUC = 0.83). SPOKEsigs outperformed predictions using EHRs alone, and the biological drivers of the classifiers provided insight into the underpinnings of prodromal MS.
conclusionUsing data from EHR as input, SPOKEsigs describe patients at both the clinical and biological levels. We provide a clinical use case for detecting MS up to 5 years prior to their documented diagnosis in the clinic and illustrate the biological features that distinguish the prodromal MS state.
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