ArticleJournal of diabetes science and technology2017
Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.
Article in Journal of diabetes science and technology, 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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Who cites it
21 citing papers in PubMed.
- An LLM-assisted framework for accelerated and verifiable clinical hypothesis testing from electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- Research on a Predictive Model for Microalbuminuria in Type 2 Diabetes Based on Machine Learning and SHAP Analysis.International journal of endocrinology · 2026Article
- Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS).BMC research notes · 2024Article
- Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women.Scientific reports · 2024Article
- Machine-learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study.Endocrinology, diabetes & metabolism · 2024Article
- Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.PloS one · 2024Article
- Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.PLoS computational biology · 2023Article
- Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.BMC public health · 2023Article
- Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Phenotyping in distributed data networks: selecting the right codes for the right patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Harnessing Digital Health Technologies to Remotely Manage Diabetic Foot Syndrome: A Narrative Review.Medicina (Kaunas, Lithuania) · 2021Review
- Performance evaluation of case definitions of type 1 diabetes for health insurance claims data in Japan.BMC medical informatics and decision making · 2021Article
- Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks.Frontiers in plant science · 2021Article
- Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.Journal of medical Internet research · 2020Article
- Polar labeling: silver standard algorithm for training disease classifiers.Bioinformatics (Oxford, England) · 2020Article
- A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.PeerJ · 2020Article
- Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods.Healthcare informatics research · 2019Review
- Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.Scientific reports · 2019Article
- Artificial Intelligence Transforms the Future of Health Care.The American journal of medicine · 2019Review
- Phenotyping to Facilitate Accrual for a Cardiovascular Intervention.Journal of clinical medicine research · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPhenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects.
objectiveWe propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects.
methodsWe employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value.
resultsThe proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects.
conclusionsWe develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users' objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.
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