ArticleNature communications2020
Inferring multimodal latent topics from electronic health records.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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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.
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Who cites it
31 citing papers in PubMed.
- Development and validation of an interpretable machine learning model using routine laboratory biomarkers to stratify severe pneumonia risk in young children.Journal of advanced research · 2026Article
- Genotype epigenome phenotype integration reveals peripheral immune contributions to type I bipolar disorder.Nature communications · 2026Article
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- PheCode-guided multi-modal topic modeling of electronic health records improves disease incidence prediction and GWAS discovery from UK Biobank.Briefings in bioinformatics · 2026Article
- Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records.medRxiv : the preprint server for health sciences · 2025Article
- Greater value add from electronic health records than polygenic risk scores for predicting myocardial infarction in machine learning.Communications medicine · 2025Article
- Foundations and Strategic Vision of the Canadian Translational Geroscience Network.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting.NPJ digital medicine · 2025Article
- Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.Journal of biomedical informatics · 2025Article
- Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation.Journal of the American Heart Association · 2024Article
- Towards new frontiers of healthcare systems research using artificial intelligence and generative AI.Health systems (Basingstoke, England) · 2024Article
- Towards a practical use of text mining approaches in electrodiagnostic data.Scientific reports · 2023Article
- GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.Genome biology · 2023Article
- Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank.Cell genomics · 2023Article
- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model.Scientific reports · 2022Article
- Integrated multimodal artificial intelligence framework for healthcare applications.NPJ digital medicine · 2022Article
- Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction.Scientific reports · 2022Article
- A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals.iScience · 2022Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.
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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.