ArticleJournal of biomedical informatics2017
EHR-based phenotyping: Bulk learning and evaluation.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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
21 citing papers in PubMed.
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- Artificial Intelligence in Biomedical Sciences: A Scoping Review.British journal of biomedical science · 2025Article
- Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysis.Research square · 2024Article
- Using a Healthcare Process Modeling Approach to Understand Electronic Health Records-based Pressure Injury Data and to Support Development of a Standardized Pressure Injury Phenotyping Pipeline.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Federated Learning in Health care Using Structured Medical Data.Advances in kidney disease and health · 2023Review
- Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.Journal of medical Internet research · 2022Article
- Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.Patterns (New York, N.Y.) · 2021Article
- High-throughput phenotyping with temporal sequences.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Generative transfer learning for measuring plausibility of EHR diagnosis records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.Journal of speech, language, and hearing research : JSLHR · 2020Article
- Electronic health records and polygenic risk scores for predicting disease risk.Nature reviews. Genetics · 2020Review
- Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations.JAMIA open · 2019Article
- High-throughput multimodal automated phenotyping (MAP) with application to PheWAS.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine.Yearbook of medical informatics · 2019Article
- Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.Annual review of biomedical data science · 2018Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- Automated disease cohort selection using word embeddings from Electronic Health Records.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
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2 authors.
Funding
Abstract
In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others. In such a framework, tasks associated with feature engineering and data annotation remain a tedious and expensive exercise, resulting in poor scalability. In addition, certain clinical conditions, such as those that are rare and acute in nature, may never accumulate sufficient data over time, which poses a challenge to establishing accurate and informative statistical models. In this paper, we use infectious diseases as the domain of study to demonstrate a hierarchical learning method based on ensemble learning that attempts to address these issues through feature abstraction. We use a sparse annotation set to train and evaluate many phenotypes at once, which we call bulk learning. In this batch-phenotyping framework, disease cohort definitions can be learned from within the abstract feature space established by using multiple diseases as a substrate and diagnostic codes as surrogates. In particular, using surrogate labels for model training renders possible its subsequent evaluation using only a sparse annotated sample. Moreover, statistical models can be trained and evaluated, using the same sparse annotation, from within the abstract feature space of low dimensionality that encapsulates the shared clinical traits of these target diseases, collectively referred to as the bulk learning set.
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