Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes

Anderson, Jeffrey P.; Parikh, Jignesh R.; Shenfeld, Daniel K.; Иванов, В.Г.; Marks, Casey; Church, Bruce W.; Laramie, Jason M.; Mardekian, Jack; Piper, B.A.; Willke, Richard J.; Rublee, Dale A.

doi:10.1177/1932296815620200

Cited by 91 publications

(67 citation statements)

References 28 publications

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“…The second category deals with disease prediction and diagnosis [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76]. Numerous algorithms and different approaches have been applied, such as traditional machine learning algorithms, ensemble learning approaches and association rule learning in order to achieve the best classification accuracy.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…The specific approaches have also been used in DM prediction [50], [52], [53], [69]. Anderson et al used a Bayesian scoring algorithm to explore the model space [50].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…The specific approaches have also been used in DM prediction [50], [52], [53], [69]. Anderson et al used a Bayesian scoring algorithm to explore the model space [50]. In [52], authors proposed an ensemble framework with multi-layer classification, using enhanced bagging and optimized weighting, combining seven heterogeneous classifiers.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

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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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Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…The specific approaches have also been used in DM prediction [50], [52], [53], [69]. Anderson et al used a Bayesian scoring algorithm to explore the model space [50].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

See 1 more Smart Citation

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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“…This is particularly important in the field of diabetes research where predictors of response to antihyperglycemic therapies, both in terms of HbA1c reduction and maintenance of glycemic control, remain elusive. One type of machine learning involves the construction of computer systems that learn from experience to identify patterns in data and predict outcomes [19].…”

Section: Application Of Machine Learning To Clinical Datasetsmentioning

confidence: 99%

Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data

et al. 2019

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Introduction Outcomes in type 2 diabetes mellitus (T2DM) could be optimized by identifying which treatments are likely to produce the greatest improvements in glycemic control for each patient. Objectives We aimed to identify patient characteristics associated with achieving and maintaining a target glycated hemoglobin (HbA1c) of ≤ 7% using machine learning methodology to analyze clinical trial data on combination therapy for T2DM. By applying a new machine learning methodology to an existing clinical dataset, the practical application of this approach was evaluated and the potential utility of this new approach to clinical decision making was assessed. Methods Data were pooled from two phase III, randomized, double-blind, parallel-group studies of empagliflozin/linagliptin single-pill combination therapy versus each monotherapy in patients who were treatment-naïve or receiving background metformin. Descriptive analysis was used to assess univariate associations between HbA1c target categories and each baseline characteristic. After the descriptive analysis results, a machine learning analysis was performed (classification tree and random forest methods) to estimate and predict target categories based on patient characteristics at baseline, without a priori selection. Results In the descriptive analysis, lower mean baseline HbA1c and fasting plasma glucose (FPG) were both associated with achieving and maintaining the HbA1c target. The machine learning analysis also identified HbA1c and FPG as the strongest predictors of attaining glycemic control. In contrast, covariates including body weight, waist circumference, blood pressure, or other variables did not contribute to the outcome. Conclusions Using both traditional and novel data analysis methodologies, this study identified baseline glycemic status as the strongest predictor of target glycemic control attainment. Machine learning algorithms provide an hypothesis-free, unbiased methodology, which can greatly enhance the search for predictors of therapeutic success in T2DM. The approach used in the present analysis provides an example of how a machine learning algorithm can be applied to a clinical dataset and used to develop predictions that can facilitate clinical decision making. Enhanced Digital Features To view enhanced digital features for this article go to https ://doi.org/10.6084/m9.figsh are.79994 78.

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“…They employed data obtained from the Canadian Primary Care Sentinel Surveillance Network (http://cpcssn.ca), which includes demographic information, body mass index (BMI), high-density lipoprotein (HDL), and triglycerides. Anderson et al (2016) analyzed different risk factors for developing T2DM in order to develop a biomarker panel. The final panel included blood sugar, age, race, triglycerides, BMI, and blood pressure.…”

Section: Introductionmentioning

confidence: 99%

The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes

Spänig

Emberger‐Klein

Sowa

et al. 2019

Artificial Intelligence in Medicine

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Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction. In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities. As a proofof-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on noninvasive sensors and deep neural networks. Moreover, the system provides an easy-tointerpret probability estimation for T2DM for a given patient. Besides the development of the AI, we further analyzed the acceptance of young people for AI in healthcare to estimate the impact of such system in the future.

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Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes

Cited by 91 publications

References 28 publications

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data

The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes

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