Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus

Pinhas‐Hamiel, Orit; Hamiel, Uri; Greenfield, Yuval; Boyko, Valentina; Graph-Barel, Chana; Rachmiel, Marianna; Lerner‐Geva, Liat; Reichman, Brian

doi:10.1002/eat.22138

Cited by 38 publications

(29 citation statements)

References 33 publications

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“…People with type 1 diabetes and eating disorders have high rates of diabetes distress and FoH (79). For people with type 1 diabetes, insulin omission causing glycosuria in order to lose weight is the most commonly reported disordered eating behavior (80,81), and in people with type 2 diabetes, bingeing (excessive food intake with an accompanying sense of loss of control) is most commonly reported. For people with type 2 diabetes treated with insulin, intentional omission is also frequently reported (82).…”

Section: Psychological Comorbiditiesmentioning

confidence: 99%

Psychosocial Care for People With Diabetes: A Position Statement of the American Diabetes Association

et al. 2016

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Section: Psychological Comorbiditiesmentioning

confidence: 99%

Psychosocial Care for People With Diabetes: A Position Statement of the American Diabetes Association

et al. 2016

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“…It is worth noting that DM complications are far less common and severe in people with well-controlled blood glucose levels. Many of those complications have been studied through machine learning and data mining applications [78], [79], [80], [81], [82], [83], [84], [85], [87], [88], [89], [90], [92], [94], [95], [96], [97].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…Although that does not occur as a diabetic complication but rather as a compensatory behavior, diagnosis of this underlying disorder is of great concern. In [97], authors used decision trees to analyze clinical and laboratory data for the prediction of intentional insulin omission for intentional weight loss.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,061

530

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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“…Adolescents with T1D struggle to maintain optimal weight compared to peers without diabetes, leading some adolescents to skip doses or take less insulin than needed to lose weight. Consistent control of diabetes is important and intentional insulin omission has potentially serious consequences, including life‐threatening diabetic ketoacidosis 5‐7…”

mentioning

confidence: 99%