ArticleJMIR medical informatics2016
Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing-Based Algorithm With Statewide Electronic Medical Records.
Article in JMIR medical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.Scientific reports · 2026Article
- Natural Language Understanding to Assess Oral Health-Related Quality of Life: A Cross-Sectional Study Incorporating a Mixed Methods Approach.Journal of oral rehabilitation · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.Healthcare (Basel, Switzerland) · 2023Review
- Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques.Frontiers in public health · 2023Article
- Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study.JMIR medical informatics · 2022Article
- A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data.Healthcare (Basel, Switzerland) · 2022Article
- Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project.JMIR medical informatics · 2022Article
- Machine and cognitive intelligence for human health: systematic review.Brain informatics · 2022Review
- Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.Frontiers in immunology · 2022Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study.JMIR formative research · 2020Article
- Machine learning models for synthesizing actionable care decisions on lower extremity wounds.Smart health (Amsterdam, Netherlands) · 2020Article
- Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.Journal of medical Internet research · 2020Article
- Evaluation of provider documentation patterns as a tool to deliver ongoing patient-centred diabetes education and support.International journal of clinical practice · 2020Article
- Automated SNOMED CT concept and attribute relationship detection through a web-based implementation of cTAKES.Journal of biomedical semantics · 2019Article
Corrections and comments
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Authors and funding
23 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency.
objectiveThis study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs).
methodsThis study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014).
resultsOf the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days).
conclusionsThe online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.
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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.