ArticleJournal of the American Medical Informatics Association : JAMIA
Importance of multi-modal approaches to effectively identify cataract cases from electronic health records.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it, 125 citations in OpenAlex.
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
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- The incremental design of a machine learning framework for medical records processing.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A general framework for developing computable clinical phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis.International ophthalmology · 2024Article
- Explainable text-tabular models for predicting mortality risk in companion animals.Scientific reports · 2024Article
- Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.Translational vision science & technology · 2024Article
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- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
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- Deep learning-based NLP data pipeline for EHR-scanned document information extraction.JAMIA open · 2022Article
- Looking for low vision: Predicting visual prognosis by fusing structured and free-text data from electronic health records.International journal of medical informatics · 2022Article
- Applications of natural language processing in ophthalmology: present and future.Frontiers in medicine · 2022Review
- Intelligent Traffic Flow Prediction and Analysis Based on Internet of Things and Big Data.Computational intelligence and neuroscience · 2022Article
- Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.Methods of information in medicine · 2021Article
- Optimizing identification of resistant hypertension: Computable phenotype development and validation.Pharmacoepidemiology and drug safety · 2020Article
- Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language.Learning health systems · 2020Article
- Imputation of Missing Data in Electronic Health Records Based on Patients' Similarities.Journal of healthcare informatics research · 2020Article
- Electronic health records and polygenic risk scores for predicting disease risk.Nature reviews. Genetics · 2020Review
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 1 country.
Funding
Abstract
objectiveThere is increasing interest in using electronic health records (EHRs) to identify subjects for genomic association studies, due in part to the availability of large amounts of clinical data and the expected cost efficiencies of subject identification. We describe the construction and validation of an EHR-based algorithm to identify subjects with age-related cataracts. MATERIALS AND
methodsWe used a multi-modal strategy consisting of structured database querying, natural language processing on free-text documents, and optical character recognition on scanned clinical images to identify cataract subjects and related cataract attributes. Extensive validation on 3657 subjects compared the multi-modal results to manual chart review. The algorithm was also implemented at participating electronic MEdical Records and GEnomics (eMERGE) institutions.
resultsAn EHR-based cataract phenotyping algorithm was successfully developed and validated, resulting in positive predictive values (PPVs) >95%. The multi-modal approach increased the identification of cataract subject attributes by a factor of three compared to single-mode approaches while maintaining high PPV. Components of the cataract algorithm were successfully deployed at three other institutions with similar accuracy. DISCUSSION: A multi-modal strategy incorporating optical character recognition and natural language processing may increase the number of cases identified while maintaining similar PPVs. Such algorithms, however, require that the needed information be embedded within clinical documents.
conclusionWe have demonstrated that algorithms to identify and characterize cataracts can be developed utilizing data collected via the EHR. These algorithms provide a high level of accuracy even when implemented across multiple EHRs and institutional boundaries.
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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.