Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

Journal of diabetes science and technology - 1 Jul 2017

Kagawa Rina, Kawazoe Yoshimasa, Ida Yusuke, Shinohara Emiko, Tanaka Katsuya, Imai Takeshi, Ohe Kazuhiko

Abstract excerpt

BACKGROUND: Phenotyping 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. OBJECTIVE: We...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Area Under Curve
Share to Diabetes Mellitus, Type 2

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Electronic Health Records

Share to Phenotype

Share to ROC Curve

Share to Sensitivity and Specificity

Share to Support Vector Machine

Source

Open in PubMed Open full text

Identifiers

PMID: 27932531
DOI: 10.1177/1932296816681584
PMCID: PMC5588819

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.PMID 27932531Read ↗

A Machine Learning-based Framework to Identify Type 2 Diabetes through Electronic Health RecordsSemantic proximity Read ↗

Development and validation of various phenotyping algorithms for Diabetes Mellitus using data from electronic health records.Semantic proximity Read ↗

A Data Analysis on Diabetic Patients Using Data Mining Tools and Prediction AlgorithmsSemantic proximity Read ↗

A Data Analysis on Diabetic Patients Using Data Mining Tools and Prediction AlgorithmsSemantic proximity Read ↗

Development and Validation of Various Phenotyping Algorithms for Diabetes Mellitus Using Data from Electronic Health Records.Semantic proximity Read ↗

Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network.Semantic proximity Read ↗

Development and validation of a type 2 diabetes machine learning classification model for EHR-based diagnostics and clinical decision supportSemantic proximity Read ↗

Towards Reliable Diabetes Prediction: Innovations in Data Engineering and Machine Learning ApplicationsSemantic proximity Read ↗

Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.Semantic proximity Read ↗

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study.Semantic proximity Read ↗

An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients.Semantic proximity Read ↗

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical ReviewSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.