Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach

2019 ◽  
pp. 103-116 ◽  
Author(s):  
Muhammad Younus ◽  
Md Tahsir Ahmed Munna ◽  
Mirza Mohtashim Alam ◽  
Shaikh Muhammad Allayear ◽  
Sheikh Joly Ferdous Ara
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Learning Approach ◽  
Machine Learning Approach

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

2016 ◽  
Vol 11 (4) ◽  
pp. 791-799 ◽  
Author(s):  
Rina Kagawa ◽  
Yoshimasa Kawazoe ◽  
Yusuke Ida ◽  
Emiko Shinohara ◽  
Katsuya Tanaka ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Expert Knowledge ◽  
The Other ◽  
Learning Approach ◽  
Research Subjects ◽  
Machine Learning Approach

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 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. Methods: 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. Results: The 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. Conclusions: We 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.

scholarly journals Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Yikang Wang ◽  
Liying Zhang ◽  
Miaomiao Niu ◽  
Ruiying Li ◽  
Runqi Tu ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Type 2 Diabetes Mellitus ◽  
Cohort Study ◽  
Prediction Model ◽  
Genetic Risk ◽  
Prediction Models

Background: Previous studies have constructed prediction models for type 2 diabetes mellitus (T2DM), but machine learning was rarely used and few focused on genetic prediction. This study aimed to establish an effective T2DM prediction tool and to further explore the potential of genetic risk scores (GRS) via various classifiers among rural adults.Methods: In this prospective study, the GRS for a total of 5,712 participants from the Henan Rural Cohort Study was calculated. Cox proportional hazards (CPH) regression was used to analyze the associations between GRS and T2DM. CPH, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) were used to establish prediction models, respectively. The area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were used to assess the discrimination ability of the models. The decision curve was plotted to determine the clinical-utility for prediction models.Results: Compared with the individuals in the lowest quintile of the GRS, the HR (95% CI) was 2.06 (1.40 to 3.03) for those with the highest quintile of GRS (Ptrend < 0.05). Based on conventional predictors, the AUCs of the prediction model were 0.815, 0.816, 0.843, and 0.851 via CPH, ANN, RF, and GBM, respectively. Changes with the integration of GRS for CPH, ANN, RF, and GBM were 0.001, 0.002, 0.018, and 0.033, respectively. The reclassifications were significantly improved for all classifiers when adding GRS (NRI: 41.2% for CPH; 41.0% for ANN; 46.4% for ANN; 45.1% for GBM). Decision curve analysis indicated the clinical benefits of model combined GRS.Conclusion: The prediction model combined with GRS may provide incremental predictions of performance beyond conventional factors for T2DM, which demonstrated the potential clinical use of genetic markers to screen vulnerable populations.Clinical Trial Registration: The Henan Rural Cohort Study is registered in the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.

1055-P: The Machine Learning Prediction Model NASHmap Identifies Higher Insulin Resistance in Type 2 Diabetes Mellitus (T2DM) Patients at Risk for Nonalcoholic Steatohepatitis (NASH)

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 1055-P
Author(s):  
ANDREAS TIETZ ◽  
GIOVANNI BADER ◽  
MATT DOCHERTY ◽  
BRENDA REINHART ◽  
MARIA-MAGDALENA BALP ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
At Risk ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Nonalcoholic Steatohepatitis ◽  
Patients At Risk

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

2021 ◽  
Vol 24 ◽  
pp. 157-166
Author(s):  
Wilailuck Tuntayothin ◽  
Stephen John Kerr ◽  
Chanchana Boonyakrai ◽  
Suwasin Udomkarnjananun ◽  
Sumitra Chukaew ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Prediction Model ◽  
Disease Prediction ◽  
Development And Validation

scholarly journals A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

2019 ◽  
Vol 43 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Brent A. Williams ◽  
Daniela Geba ◽  
Jeanine M. Cordova ◽  
Sharash S. Shetty
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Heart Failure Hospitalization
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