ArticleJournal of the American Medical Informatics Association : JAMIA2020
Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.
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 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.The American journal of gastroenterology · 2024Pooled it
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- Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations.Life (Basel, Switzerland) · 2025Review
- Selective prediction for extracting unstructured clinical data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
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- Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer.Patterns (New York, N.Y.) · 2023Article
- A nomogram for predicting echocardiogram prescription in outpatients: an analysis of the NAMCS database.Frontiers in cardiovascular medicine · 2023Article
- An Extensive Data Processing Pipeline for MIMIC-IV.Proceedings of machine learning research · 2022Article
- Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care-Associated Infection.JMIR medical informatics · 2022Article
- Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.The Lancet. Digital health · 2022Article
- ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.BMC medical research methodology · 2022Article
- The Mass General Brigham Biobank Portal: an i2b2-based data repository linking disparate and high-dimensional patient data to support multimodal analytics.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.Annual review of biomedical data science · 2021Review
- Augmenting aer2vec: Enriching distributed representations of adverse event report data with orthographic and lexical information.Journal of biomedical informatics · 2021Article
- The UMLS knowledge sources at 30: indispensable to current research and applications in biomedical informatics.Journal of the American Medical Informatics Association : JAMIA · 2020Article
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7 authors.
Funding
Abstract
objectivePredictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning. MATERIALS AND
methodsWe projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks: the risk prediction of heart failure in diabetes patients and the risk prediction of pancreatic cancer. Two popular models were evaluated: logistic regression and a recurrent neural network.
resultsFor logistic regression, using UMLS delivered the optimal area under the receiver operating characteristics (AUROC) results in both dengue hemorrhagic fever (81.15%) and pancreatic cancer (80.53%) tasks. For recurrent neural network, UMLS worked best for pancreatic cancer prediction (AUROC 82.24%), second only (AUROC 85.55%) to PheWAS (AUROC 85.87%) for dengue hemorrhagic fever prediction. DISCUSSION/
conclusionIn our experiments, terminologies with larger vocabularies and finer-grained representations were associated with better prediction performances. In particular, UMLS is consistently 1 of the best-performing ones. We believe that our work may help to inform better designs of predictive models, although further investigation is warranted.
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