Predicting Hypoglycemia in Diabetic Patients Using Time-Sensitive Artificial Neural Networks

Eljil, Khouloud Safi; Qadah, Ghassan Z.; Pasquier, M.

doi:10.4018/978-1-7998-0414-7.ch069

Cited by 6 publications

(10 citation statements)

References 18 publications

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“…Of the 33 studies, 19 studies (58%) [ 26 - 31 , 33 , 35 , 36 , 38 - 42 , 44 - 47 , 54 ] predicted hypoglycemia, and the remaining 14 studies (42%) detected hypoglycemia [ 15 , 20 , 25 , 32 , 34 , 37 , 43 , 48 - 53 , 55 ]. As much as 25 of the 33 included studies (76%) [ 15 , 20 , 25 - 27 , 29 , 30 , 32 , 35 , 36 , 38 , 39 , 41 - 44 , 46 - 53 , 55 ] specified type 1 as the type of DM. Type 2 DM was specified in only 3 of these studies (9%) [ 28 , 31 , 45 ] and the remaining 5 studies [ 33 , 34 , 37 , 40 , 54 ] did not specify the type of DM.…”

Section: Resultsmentioning

confidence: 99%

“…Multimedia Appendix 5 shows the profiling data input into the ML algorithm for testing its performance in detecting or predicting hypoglycemia. In the majority of the 19 studies for predicting hypoglycemia (13 studies; 68%) [ 26 - 30 , 35 , 36 , 38 , 40 - 42 , 46 , 47 ], historical CGM data were input into the ML algorithm while the remaining 6 studies (32%) [ 31 , 33 , 39 , 44 , 45 , 54 ] did not use CGM. Of the 14 studies that detected hypoglycemia using ML, 7 studies (50%) [ 20 , 25 , 32 , 49 , 50 , 52 , 55 ] used information from electroencephalograms (EEGs) and 4 studies (29%) [ 15 , 43 , 51 , 53 ] used results of electrocardiography (ECG).…”

Section: Resultsmentioning

confidence: 99%

“…Mean score (SD) was 5.6 (1.1), which corresponded to 80% of full marks (=7). The applicability of the reference test was evaluated to be low in 61% of the 33 included studies (20 studies) because hypoglycemia was not diagnosed by measuring blood glucose levels or ICD codes but by CGM (ie, glucose levels in blood are indirectly estimated from those in interstitial tissue) (19 studies) [ 15 , 26 - 30 , 35 , 38 , 40 - 43 , 46 , 47 , 49 - 52 , 54 ] or experts’ subjective judgement (1 study) [ 34 ]. The 2 factors were mainly responsible for lowering the study quality.…”

Section: Resultsmentioning

confidence: 99%

“…The 2 factors were mainly responsible for lowering the study quality. We considered that the threshold of hypoglycemia in the index test was not specified in 7 studies, which used the cross-validation method [ 26 , 28 , 33 , 34 , 37 , 40 , 46 ], and 1 study, which used the leave-one-out method to separate test data from training data [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

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Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

et al. 2021

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Background Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. Objective The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). Methods Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. Results A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. Conclusions Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

et al. 2021

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“…Choosing the best suited and optimized training algorithm for training the network is a crucial step since it a®ects the time required to train the model, its accuracy, precision, and requirement of computing power. 30,31 3. ANN and Other Preliminaries…”

Section: Related Workmentioning

confidence: 99%

A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India

Mandal

Ghosh

Shit

et al. 2020

Vietnam J. Comp. Sci.

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Various training algorithms are used in artificial neural networks for updating the weights during training the network. But, the selection of the appropriate training algorithm is dependent on the input–output mapping of dataset for which the network is constructed. In this paper, a framework has been proposed consisting of five modules to select the optimal training algorithm for predicting pig breeds from their images. The individual pig images from five pig-breeds have been captured using inbuilt camera of mobile phone and the contour of pig has been segmented from each captured image by HUE-based segmentation algorithm. In Statistical Parameter and Color Component retrieval module, parameters like entropy, standard deviation, variance, mean, median, and mode and color properties like hue, saturation, value (HSV) extracted from the content of each segmented image. Values of all extracted parameters have been transferred into Training Algorithm Selection Module. In this module, a fitting neural network with different numbers of hidden neurons has been executed by feeding all extracted values from pig images for mapping their breeds. Ten training algorithms have been applied on the same extracted dataset separately for five epochs each keeping other network parameters constants. The mean square error (MSE) and correlation coefficient ([Formula: see text]) for the validation set have been calculated after adjustment of weights and biases in each connection of the neurons. One training algorithm among 10 and its suitable number of hidden neurons has been selected based on comparative analysis for getting lower MSE and higher [Formula: see text] in the validation set. Then, the fitting network with selected training algorithm has been run on the same extracted datasets until the stopping condition is reached. Then the test set images are fed into the network and the network output has been categorized to class which has been assigned to each breed of pig in Breed Prediction Module. The proposed framework has been able to predict breeds with 96.00% accuracy, achieved by the trial with 50 images of the test set. It may be concluded that the Neuro Statistic Neural Network model may be used for breed prediction of pigs by using images of individual pigs.

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Using machine learning to predict severe hypoglycaemia in hospital

Fralick

Dai²,

Pou-Prom³

et al. 2021

Diabetes Obesity Metabolism

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Aim To predict the risk of hypoglycaemia using machine‐learning techniques in hospitalized patients. Methods We conducted a retrospective cohort study of patients hospitalized under general internal medicine (GIM) and cardiovascular surgery (CV) at a tertiary care teaching hospital in Toronto, Ontario. Three models were generated using supervised machine learning: least absolute shrinkage and selection operator (LASSO) logistic regression; gradient‐boosted trees; and a recurrent neural network. Each model included baseline patient data and time‐varying data. Natural‐language processing was used to incorporate text data from physician and nursing notes. Results We included 8492 GIM admissions and 8044 CV admissions. Hypoglycaemia occurred in 16% of GIM admissions and 13% of CV admissions. The area under the curve for the models in the held‐out validation set was approximately 0.80 on the GIM ward and 0.82 on the CV ward. When the threshold for hypoglycaemia was lowered to 2.9 mmol/L (52 mg/dL), similar results were observed. Among the patients at the highest decile of risk, the positive predictive value was approximately 50% and the sensitivity was 99%. Conclusion Machine‐learning approaches can accurately identify patients at high risk of hypoglycaemia in hospital. Future work will involve evaluating whether implementing this model with targeted clinical interventions can improve clinical outcomes.

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Predicting Hypoglycemia in Diabetic Patients Using Time-Sensitive Artificial Neural Networks

Cited by 6 publications

References 18 publications

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

A Framework for Selection of Training Algorithm of Neuro-Statistic Model for Prediction of Pig Breeds in India

Using machine learning to predict severe hypoglycaemia in hospital

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