1311-P: Machine Learning (ML) Application to Predict Patient Risk of Nonadherence in Type 2 Diabetes Management Using U.S. Claims Databases

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1311-P
Author(s):  
XIN CHEN ◽  
GAIL FERNANDES ◽  
JIE CHEN ◽  
ZHIWEN LIU ◽  
RICHARD BAUMGARTNER
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Diabetes Management ◽  
Patient Risk

scholarly journals Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis

2020 ◽  
Vol 8 (1) ◽  
pp. e001362
Author(s):  
Carlo Bruno Giorda ◽  
Federico Pisani ◽  
Alberto De Micheli ◽  
Paola Ponzani ◽  
Giuseppina Russo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Weight Gain ◽  
Metabolic Control ◽  
Diabetes Management ◽  
Microvascular Complications ◽  
Clinical Inertia ◽  
Oral Agents

IntroductionThe aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes.Research design and MethodsOverall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005–2017 data from 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain.ResultsThe combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control.ConclusionsTreating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention.

Frequency of Diabetes-Related Complications in Type 1 and Type 2 Diabetes—Results from the International Diabetes Management Practices Study (IDMPS)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1584-P
Author(s):  
JUAN J. GAGLIARDINO ◽  
PABLO ASCHNER ◽  
HASAN M. ILKOVA ◽  
FERNANDO J. LAVALLE-GONZALEZ ◽  
AMBADY RAMACHANDRAN ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Management Practices ◽  
Diabetes Management

1158-P: Type 2 Diabetes Patients Subtype Analysis: Unbiased Machine Learning for Future Personalized T2D Care

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1158-P
Author(s):  
LI CHEN ◽  
LINGGE FENG ◽  
CUI TANG ◽  
YI ZHANG
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
P Type ◽  
Diabetes Patients

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

The Role of Disordered Eating in Type 2 Diabetes: A Pilot Study

2021 ◽  
pp. 155982762110024
Author(s):  
Alyssa M. Vela ◽  
Brooke Palmer ◽  
Virginia Gil-Rivas ◽  
Fary Cachelin
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Disordered Eating ◽  
Lifestyle Intervention ◽  
Eating Behaviors ◽  
Diabetes Management ◽  
Behavioral Therapy ◽  
Exercise Stress ◽  
Increased Risk

Rates of type 2 diabetes mellitus continue to rise around the world, largely due to lifestyle factors such as poor diet, overeating, and lack of physical activity. Diet and eating is often the most challenging aspect of management and, when disordered, has been associated with increased risk for diabetes-related complications. Thus, there is a clear need for accessible and evidence-based interventions that address the complex lifestyle behaviors that influence diabetes management. The current study sought to assess the efficacy and acceptability of a pilot lifestyle intervention for women with type 2 diabetes and disordered eating. The intervention followed a cognitive behavioral therapy guided-self-help (CBTgsh) model and included several pillars of lifestyle medicine, including: diet, exercise, stress, and relationships. Ten women completed the 12-week intervention that provided social support, encouraged physical activity, and addressed eating behaviors and cognitions. Results indicate the lifestyle intervention was a feasible treatment for disordered eating behaviors among women with type 2 diabetes and was also associated with improved diabetes-related quality of life. The intervention was also acceptable to participants who reported satisfaction with the program. The current CBTgsh lifestyle intervention is a promising treatment option to reduce disordered eating and improve diabetes management.

Type 2 Diabetes Management Algorithms?

2016 ◽  
Vol 18 (6) ◽  
pp. 339-340 ◽  
Author(s):  
Subhash Kumar Wangnoo
Keyword(s):  
Type 2 Diabetes ◽  
Diabetes Management ◽  
Management Algorithms

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.

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