ArticleInternational journal of medical informatics2013
The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects.
Article in International journal of medical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Making human derived data FAIR: feedback from NCI office of data sharing workshop.Journal of the National Cancer Institute · 2025Article
- Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A case study in applying artificial intelligence-based named entity recognition to develop an automated ophthalmic disease registry.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data.Frontiers in medicine · 2023Article
- Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics).The journal of allergy and clinical immunology. In practice · 2022Review
- Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations.Journal of the American Heart Association · 2022Article
- Constructing Epidemiologic Cohorts from Electronic Health Record Data.International journal of environmental research and public health · 2021Article
- Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort.Journal of biomedical informatics · 2021Article
- A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19.Journal of biomedical informatics · 2021Article
- Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies.Journal of clinical medicine · 2020Article
- Impact of Diverse Data Sources on Computational Phenotyping.Frontiers in genetics · 2020Article
- A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing.IEEE journal of translational engineering in health and medicine · 2020Article
- Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction.Scientific reports · 2019Article
- Careflow Mining Techniques to Explore Type 2 Diabetes Evolution.Journal of diabetes science and technology · 2018Article
- Biases introduced by filtering electronic health records for patients with "complete data".Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Article
- A machine learning-based framework to identify type 2 diabetes through electronic health records.International journal of medical informatics · 2017Article
- 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.JMIR medical informatics · 2016Article
- Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.Journal of the American Medical Informatics Association : JAMIA · 2016Article
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Authors and funding
5 authors.
Funding
Abstract
purposeTo evaluate the impact of insufficient longitudinal data on the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm for identifying (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes.
methodsRetrospective study conducted at Mayo Clinic in Rochester, Minnesota. Eligible subjects were Olmsted County residents with ≥1 Mayo Clinic encounter in each of three time periods: (1) 2007, (2) from 1997 through 2006, and (3) before 1997 (N = 54,283). Diabetes relevant electronic medical record (EMR) data about diagnoses, laboratories, and medications were used. We employed the HTCP algorithm to categorize individuals as T2DM cases and non-diabetes controls. Considering the full 11 years (1997-2007) as the gold standard, we compared gold-standard categorizations with those using data for 10 subsequent intervals, ranging from 1998-2007 (10-year data) to 2007 (1-year data). Positive predictive values (PPVs) and false-negative rates (FNRs) were calculated. McNemar tests were used to determine whether categorizations using shorter time periods differed from the gold standard. Statistical significance was defined as P < 0.05.
resultsWe identified 2770 T2DM cases and 21,005 controls when the algorithm was applied using 11-year data. Using 2007 data alone, PPVs and FNRs, respectively, were 70% and 25% for case identification and 59% and 67% for control identification. All time frames differed significantly from the gold standard, except for the 10-year period.
conclusionsThe accuracy of the algorithm reduced remarkably as data were limited to shorter observation periods. This impact should be considered carefully when designing/executing HTCP algorithms.
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