ArticleJournal of the American Medical Informatics Association : JAMIA2020
sureLDA: A multidisease automated phenotyping method for the electronic health record.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing.Scientific reports · 2026Article
- ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.Journal of biomedical informatics · 2025Article
- Automated Shared Phenotype Discovery in Undiagnosed Cohorts for Rare Disease Research.Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · 2024Article
- LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.Patterns (New York, N.Y.) · 2024Article
- GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.Genome biology · 2023Article
- Semisupervised Calibration of Risk with Noisy Event Times (SCORNET) using electronic health record data.Biostatistics (Oxford, England) · 2023Article
- Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.Journal of medical Internet research · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model.Scientific reports · 2022Article
- A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.Scientific reports · 2022Article
- Weakly Semi-supervised phenotyping using Electronic Health records.Journal of biomedical informatics · 2022Article
- SAT: a Surrogate-Assisted Two-wave case boosting sampling method, with application to EHR-based association studies.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.Patterns (New York, N.Y.) · 2021Article
- Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method.JMIRx med · 2021Article
- Key Contributions in Clinical Research Informatics.Yearbook of medical informatics · 2021Review
- Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies.NPJ digital medicine · 2021Article
- Celebrating G. Octo Barnett, MD.Journal of the American Medical Informatics Association : JAMIA · 2020Article
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Authors and funding
9 authors.
Funding
Abstract
objectiveA major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes. MATERIALS AND
methodsSurrogate-guided ensemble latent Dirichlet allocation (sureLDA) is a label-free multidimensional phenotyping method. It first uses the PheNorm algorithm to initialize probabilities based on 2 surrogate features for each target phenotype, and then leverages these probabilities to constrain the LDA topic model to generate phenotype-specific topics. Finally, it combines phenotype-feature counts with surrogates via clustering ensemble to yield final phenotype probabilities.
resultssureLDA achieves reliably high accuracy and precision across a range of simulated and real-world phenotypes. Its performance is robust to phenotype prevalence and relative informativeness of surogate vs nonsurrogate features. It also exhibits powerful feature selection properties. DISCUSSION: sureLDA combines attractive properties of PheNorm and LDA to achieve high accuracy and precision robust to diverse phenotype characteristics. It offers particular improvement for phenotypes insufficiently captured by a few surrogate features. Moreover, sureLDA's feature selection ability enables it to handle high feature dimensions and produce interpretable computational phenotypes.
conclusionssureLDA is well suited toward large-scale electronic health record phenotyping for highly multiphenotype applications such as phenome-wide association studies .
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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.