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

Journal of Diabetes Science and Technology
Rina KagawaKazuhiko Ohe

Abstract

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. We 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. We 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. The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can ...Continue Reading

Citations

Jan 7, 2000·American Journal of Medical Quality : the Official Journal of the American College of Medical Quality·P L HebertA M McBean
Sep 18, 2015·The New England Journal of Medicine·Bernard ZinmanEMPA-REG OUTCOME Investigators

Related Concepts

Support Vector Machines
Area Under Curve
Diabetes Mellitus, Non-Insulin-Dependent
Electronic Health Records
Research
Classification
Evaluation
Receiver Operating Characteristic
Extraction
Dispensing Medication

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